If you or someone you know is interested in showcasing their research during our poster session please visit our submission page - The deadline to submit is Friday10/26

Representatives from the humanitarian sector, government, NGOs, foundations and private industry, and academia present diverse perspectives in health and humanitarian challenges through keynote addresses, panel discussions, focused workshops, lunchtime group discussions, and interactive poster sessions covering a broad set of research topics and applications.

This is the first time, the conference will be hosted in Dubai, a staple humanitarian hub, and it is sure to bring together an abundance of professionals active in the global health and humanitarian sectors from around the world.

Visit the conference website at https://chhs.gatech.edu/conference/2018. 

Relief requirements for public health and humanitarian events are in general both unknown in size and type, and are affected by dynamic and hard to measure factors such as geographic location, local economy, infrastructure, social and political conditions, etc. Preparing for long-term development and response to emergent events often involves uncertainty in timing, scope, or scale. Pre-planning for these situations requires an understanding of forecasting, distribution network design, and strategies for managing the uncertainty. This course will examine methods and models for making pre-planning decisions and explore the significant value that is obtained through informed decision-making in advance of an unpredictable event.

WHO SHOULD ATTEND

Tactical and strategic members of non-governmental organizations and private corporations involved in the humanitarian relief efforts, U.S. federal government employees, U.S. state or local government employees, humanitarian foundation members, program managers, directors of field operations, disaster relief managers, director of logistics and inventory, and logistics officers

HOW YOU WILL BENEFIT

• Develop and apply analytical models to aid in distribution and routing decisions under uncertain conditions.
• Identify and apply the appropriate forecasting technique considering characteristics such as seasonality and trends.
• Provide immediate impact to their organization through learnings gained from applied and real-world case studies.
• Learn to bring NGOs, businesses or government entities together to enhance collaboration, cooperation and communication.
• Discover current trends and procedures to help their organizations and team members get ahead and stay ahead of the curve.
• Build a critical knowledge base to make tactical decisions around inventory, routing and distribution.
• Deliver best-practices to measure and evaluate outcomes of humanitarian relief efforts.
• Ultimately transform the humanitarian sector with increased capacity to participate in planning and strategic decision-making for effective supply chain management.

WHAT IS COVERED

Pre-Course Activities (2.5 hrs) - Online via HELIX platform
Classroom Activities (2 days) - Georgia Tech Global Learning Center

NOTE: Pre-course activities will conducted online using the HELIX online learning management system. Access instructions will be provided to registrants when details become available.

Pre-Course Activities - Conducted online via HELIX

• Distribution Network Design – 2 hours
• Forecasting – 1.5 hours

Classroom Activities

Day 1

• Overview of Systems Design for Humantiarian Logistics
• Distribution and Network Design
• Pre-positioning

Day 2

• Forecasting
• Managing Uncertainty

COURSE MATERIALS

Provided

• Participants receive readings, case studies, spreadsheet files, and lecture slides, and will be given access to the pre-course web-based activities.

Recommended

• Students need a laptop with Microsoft Excel and the ability to connect to a high-speed internet connection (internet access is provided for onsite portions of course).

STUDENT REQUIREMENTS

Students need a laptop with Microsoft Excel and the ability to connect to a high-speed internet connection (internet access is provided for onsite portions of course).

COURSE PREREQUISITES

For those interested in earning the Health and Humanitarian Supply Chain Management Certificate, this course is the first of the three-course certificate program. To earn the certificate, participants must register and complete the following courses within three years:

1. Pre-planning Strategy for Health and Humanitarian Organizations
2. Tactical Decision Making in Public Health and Humanitarian Response
3. Systems Operations in Health and Humanitarian Response

COURSE INSTRUCTORS

Julie Swann, Ozlem Ergun, Pinar Keskinocak

IPIC 2018 aims to provide an open forum for researchers, industry representatives, government officials and citizens to together explore, discuss, introduce leading edge concepts, methodologies, recent projects, technological advancements,start-up initiatives, for current and future Physical Internet implementation.

Conference topics include inter-connected logistics, PI fundamentals, business models, governance and implementation, cross-chain control, synchromodal transportation, IT systems, stakeholders and their roles. New business models, enabling technologies and experimentations already underway will be presented, making this meeting a unique opportunity to learn, network and discuss the latest results and challenges about interconnected logistics. And, because logistics is global, participants will be from all over the world including researchers, industrial and international institution members, local authorities and standardization committees.

Visit www.pi.events to learn more about the conference.

ASSOCIATED ACTIVITIES

Doctoral Colloquium

The Physical Internet Doctoral Colloquium provides PhD candidates with the opportunity to present and discuss their research with fellow PhD candidates and established researchers. One of the key elements of the seminar will be to discuss positioning of the PhD project in well-known and established lines of research and linking it in a founded way to the physical internet. Participation in the colloquium is by invitation. PhD candidates are asked to submit a research proposal or a working paper for review, along with a letter of recommendation. A limited number of grants will be made available to excellent PhD candidates to cover travel and hotel costs.

Student Competition

Bachelor and master students will be invited to join in an international competition to work on an assignment related to the physical internet. The assignment will be made available Fall 2017. A jury will select the winning team. A limited number of grants will be made available to the best team to visit the conference and present the work submitted.

Strategic and Analytics-Driven Inspection Operations for Infrastructure Resilience

Abstract:

This talk addresses the design of optimal inspection and response strategies to improve the resilience of critical infrastructure networks to failure events resulting from cybersecurity attacks and/or natural events. For strategic planning, we consider the problem of finding an inspection strategy utilizing the minimum number of smart detectors to ensure a target detection performance against multiple adversarial disruptions. This problem can be formulated as a mathematical program with constraints involving Nash equilibria of a large strategic game. We develop a scalable approach that computes randomized strategies based on solutions of a minimum set cover problem and a maximum set packing problem, along with optimality guarantees. For operational response in the aftermath of a natural disaster, we consider a stochastic orienteering problem with probing constraints to localize network failures. We first develop a predictive failure model using data from San Francisco Bay area’s gas pipeline inspection operations. Then, we exploit the problem structure to design a scalable non-adaptive algorithm based on integer programming that can be solved for large-scale instances. Our results lead to practical strategies for response operations, with small optimality gaps. We demonstrate the value of utilizing real-world failure data and network properties for improving inspection and response operations.

Bio:

Mathieu Dahan is a PhD candidate in the Center for Computational Engineering at MIT. Mathieu earned a M.S. in Computation for Design and Optimization from MIT, an M.Eng. and B.Eng. in Applied Mathematics from École Centrale Paris, and a B.S. in Mathematics from the University of Paris Sud. His research interests are in combinatorial optimization, game theory, and predictive analytics, with applications to service operations and disaster logistics. His work in strategic network inspection is aimed at detecting cybersecurity threats to critical infrastructure systems. He is collaborating with utility companies on the use of mobile sensors and predictive analytics for improving disaster response operations. Previously, he worked as a research scientist intern at Amazon.

Investment Effects of Pricing Schemes for Non-Convex Markets

Abstract:

A major motivation for competitive markets in electricity is their potential to coordinate efficient entry and exit of generation resources. The efficiency of these decisions depends on the availability of transparent and complete price signals. Determining appropriate prices in non-convex markets, however, is not a straightforward task. To help resolve the incentive compatibility issues that arise when clearing these markets, operators have introduced a variety of price formation and uplift payment schemes. In this talk, we develop a two-stage capacity expansion model to investigate the impact that the choice of pricing scheme can have on generation investment decisions. Our results suggest that despite the presence of fixed cost elements, prices derived from marginal costs support the optimal capacity mix. The use of uplift payments to supplement these prices could lead to significant distortion of the capacity mix arising in competitive markets. Judicious implementation of enhanced price formation schemes, to the extent they eliminate the need for discriminatory side payments, may enable system operators to alleviate this distortion.

Biography:

Jacob Mays is a PhD Candidate in the Department of Industrial Engineering & Management Sciences at Northwestern University. His research focuses on applications of stochastic optimization and statistical learning in power systems. Jacob holds an AB in chemistry and physics from Harvard University and an MEng in energy systems from the University of Wisconsin–Madison. He has also worked as a management consultant, primarily in the aviation and rail industries, and at the Federal Energy Regulatory Commission in Washington, DC.

SCL IRC Seminar: Smart GT - Achieving Smart Communities Development at Georgia Tech
Dr. Debra Lam
RSVP by August 24, 2018

Whether measured by expected market valuation, speed of technological change, or potential of data collection and analytics, smart cities development has become a vital area of growth for governments, the public and corporations a like. However, the development has not been as inclusive and more action needs to be undertaken to ensure that all communities can reap the benefits of smart development. This talk will explore what smart cities means for Georgia Tech, how GT is broaden the opportunities for smart communities, including the current and upcoming projects, and further plans to  drive smart community thought leadership.

Register Online for upcoming SCL IRC seminars

Attendance to the sessions is complimentary for SCL corporate partners, SCL Industry Advisory Board members, SCL affiliated faculty and students, and students enrolled in the Masters in Supply Chain Engineering program.

To take advantage of the included lunch, you must register by deadline noted on the registration page.

If you have any questions, please email event@scl.gatech.edu.

Applications of IOT Technology in the Supply Chain
Dr. Russell Clark
RSVP by January 19, 2018

The commoditization of sensor packages along with ubiquitous wireless communications has combined to make the Internet of Things a reality. The supply chain and logistics fields are undergoing dramatic changes based from the introduction of these technologies. We will look at some examples and opportunities to leverage the widespread availability of fine-grained, real-time data. We will also discuss challenges that must be addressed as these technologies are adopted.

Register Online for upcoming SCL IRC seminars

Applications of Machine Learning in the Supply Chain
Dr. Sebastian Pokutta
RSVP by February 15, 2018

In this talk we will explore the possibilities of machine learning in supply chains and logistics. We will see that modern machine learning methods, often in a black box fashion, allow us to move from model-driven decision-making to data-driven decision-making. This results in high levels of flexibility and agility, which is of ever-increasing importance in fast-paced supply chain and logistics environments. We will lay out the foundation of this paradigm and look at specifics examples from the supply chain and logistics context.

Register Online for upcoming SCL IRC seminars

SCL IRC Seminar: Supply Chain Innovation Showcase
Amari Ruff/Sudu​, Paul Noble/Autit
RSVP by September 21, 2018

A special session where two early stage companies will talk individually about their unique characteristics/problems solved. Our speaker from Sudu will present "How startups are pushing the Future of Transportation" to discuss how technology is making its way into the transportation industry and changing the way large corporations do business. Our speaker from Autit will present "The Intelligent Manufacturing Enterprise" where we will discuss the opportunities for frontier technologies (AI/ML) to enhance Legacy ERP platforms and how Autit is leading the way with data harmonization and predictive inventory for parts in manufacturing supply chains.

Register Online for upcoming SCL IRC seminars

Attendance to the sessions is complimentary for SCL corporate partners, SCL Industry Advisory Board members, SCL affiliated faculty and students, and students enrolled in the Masters in Supply Chain Engineering program.

To take advantage of the included lunch, you must register by deadline noted on the registration page.

If you have any questions, please email event@scl.gatech.edu.

SCL IRC Seminar: Operational and Tactical Analysis of Same-Day Delivery Distribution Systems ​
Alejandro Toriello
RSVP by October 19, 2018

E-retail is a highly competitive segment that constantly demands innovation and process improvement. One such innovation rapidly gaining traction is same-day delivery (SDD); large e-retailers like Amazon are quite active in SDD, and over half of all US retailers claim to offer some form of the service. This talk surveys some of our recent and ongoing work studying the distribution component of SDD systems, and includes both (1) operational questions faced by dispatchers, e.g. when should vehicles be dispatched and what orders should they deliver, as well as (2) tactical questions faced by managers, e.g. how late in the day should SDD be offered and how big should the delivery fleet be. The talk is based on join work with Alan Erera, Mathias Klapp (now at PUC Chile) and Alex Stroh.

Register Online for upcoming SCL IRC seminars

Attendance to the sessions is complimentary for SCL corporate partners, SCL Industry Advisory Board members, SCL affiliated faculty and students, and students enrolled in the Masters in Supply Chain Engineering program.

To take advantage of the included lunch, you must register by deadline noted on the registration page.

If you have any questions, please email event@scl.gatech.edu.

Abstract: In today’s urban mobility marketplaces, both operational policies (e.g., matching, routing) and economic policies (e.g., pricing, incentives) affect perceptions of Quality of Service (QoS) and users’ mobility choices. These, in turn, affect both operational objectives (e.g., utilization, vehicle-miles travelled) and economic objectives (e.g., profit, welfare). We study such complex interactions between behavior, economics, and operations in two settings. First, for a commercial ridesharing service provider, we study the problem of pricing exclusive vs. pooled service to maximize profit, while incorporating QoS guarantees (motivated by recent field experiments that highlight the importance of proactively compensating users when they experience frustrations during service). We introduce a dynamic notion of QoS, called sequential individual rationality, that captures users’ behavior-driven responses to the sequence of utilities that they experience during the successive stages of a shared ride, as new users are added. Subsequent analysis of the provider’s QoS-sensitive profit not only yields the optimal QoS-aware pricing policy, but also reveals key operational insights, such as an elegant characterization of optimal “shareable regions”. Our framework is general and can be, for example, adapted to the peer-to-peer carpooling context, where the problem involves finding QoS-aware, fair cost sharing schemes. (This part of the talk is based on joint work with Theja Tulabandhula and Koyel Mukherjee.) Next, we shift our focus from a commercial to a societal perspective, where we study the problem of designing a centralized market for urban mobility that increases social welfare, while ensuring voluntary participation from multiple stakeholders (such as the commuters, non-profit public transit agencies, and profit-sensitive on-demand service providers). We show that multi-modal travel, as an operational handle, can significantly soften the dependence of such mechanisms on external subsidies (mandated by fundamental impossibility results in mechanism design), by favorably exploiting structural properties of a city’s transportation network. (This part of the talk is based on ongoing work with Chamsi Hssaine, Siddhartha Banerjee, and Samitha Samaranayake.) Our results in both settings emphasize the importance of an integrated approach for tackling the next generation of challenges facing urban mobility.

Bio: Raga Gopalakrishnan is a postdoctoral associate in the School of Civil and Environmental Engineering, jointly with the School of Operations Research and Information Engineering, at Cornell University. His research concerns the design of service systems with strategic entities, especially the interplay between operational policies and economic mechanisms, with a growing focus on applications to urban transportation. His interdisciplinary work emphasizes a practice-aware approach to operations research, and combines tools from multiple subjects such as optimization, probability, queueing theory, decision theory, game theory and mechanism design. Raga obtained his Ph.D. from the Department of Computing and Mathematical Sciences at California Institute of Technology in 2013, following which he was a research associate at the University of Colorado Boulder, and a research scientist and manager at Xerox Research.

Sequential Decision-Making Under Ambiguity with Applications to Chronic Disease Management

Abstract:

Optimization of sequential decision-making under uncertainty is important in many contexts, including chronic diseases, but ambiguity in the underlying models introduces significant challenges. In the context of chronic disease management, Markov decision processes (MDPs) have been used to optimize the delivery of medical interventions in a way that balances the immediate harms and costs with the uncertain future health benefits associated with these interventions. Unfortunately, treatment recommendations that result from MDPs can depend heavily on the model of the chronic disease, and there are often multiple plausible models due to conflicting data sources or differing opinions among medical experts. To address this problem, we introduce a new framework in which a decision-maker can consider multiple models of the MDP’s ambiguous parameters and seeks to find a strategy that maximizes the weighted performance with respect to each of these models of the MDP. We establish connections to other models in the stochastic optimization literature, derive complexity results, and establish solution methods for solving these problems. We illustrate our approach in the context of preventative treatment for cardiovascular disease, and end with a discussion of opportunities for future work by extending to other preferences towards ambiguity and other chronic diseases.

Bio:

Lauren Steimle is a Ph.D. candidate in Industrial and Operations Engineering at the University of Michigan. Her research interests are in operations research and data analytics with a focus on computational optimization and stochastic modeling for solving decision-making problems under uncertainty with applications to public health. Steimle received her M.S.E. in Industrial and Operations Engineering from the University of Michigan in 2016 and holds a B.S. in Systems Science and Engineering from Washington University in St. Louis. She is the recipient of the National Science Foundation Graduate Student Research Fellowship, a member of the third-place team in the New England Journal of Medicine’s SPRINT Data Challenge, and an Honorable Mention for the Ford Foundation Predoctoral Fellowship. She has served as President of the University of Michigan’s INFORMS Student Chapter, Student Chapter Representative on the INFORMS Subdivision Council, and Outreach Officer of the Graduate Society of Women Engineers at Michigan.

Abstract:

The dynamic facility layout problem (DFLP) is to decide the locations of the departments in a facility over multiple planning periods. The main challenge in the DFLP is that there are two conflicting objectives of the problem: minimizing the material handling cost and the rearrangement cost. The cost of changing the structure of the layout over the planning horizon is also considered. Therefore, we model and solve the unequal area zone-based DFLP (ZDFLP) where the dimensions of the departments are decision variables and the departments are assigned to flexible zones with a pre-structured positioning. A zone-based block layout inherently includes possible aisle structures which can easily be adapted to the material handling system in use. This is particularly important in the DFLP because the changes in a block layout may require structural modifications in the material handling system, which in turn may be very costly. The proposed approach also considers determining relative department locations, their dimensions, as well as input/output (I/O) points concurrently for the first time in the literature. A new matheuristic, which combines concepts from simulated annealing, variable neighborhood search, and mixed-integer programming, is used to solve the ZDFLP with promising results.

Sadan Kulturel‐Konak is a Professor of Management Information Systems and the Director of Flemming Creativity, Entrepreneurship and Economic Development (CEED) Center at Penn State Berks.  She received her Ph.D. in Industrial and Systems Engineering from Auburn University.  Her research interests are in modeling and optimization of complex systems and robustness under uncertainty with applications to facility layout, reliability, and scheduling.  She has published her research in numerous journals including IIE Transactions, OR Letters, INFORMS Journal on Computing, INFORMS Transactions on Education, International Journal of Production Research, European Journal of Operational Research, and Journal of Intelligent Manufacturing Computers.  She is a member of the Institute for Operations Research and the Management Sciences (INFORMS), the Institute of Industrial and Systems Engineers (IISE) and the American Society for Engineering Education (ASEE).  She is the past chairs of the Women in OR/MS (WORMS), INFORMS- Facility Logistics Special Interest Group and ASEE Middle Atlantic Section.  She has been a principal investigator in sponsored projects from National Science Foundation (NSF) and VentureWell.  sadan@psu.edu.

Lecture on Combinatorial Statistics

Abstract: In these lectures we discuss some statistical problems with an interesting combinatorial structure behind. We start by reviewing the "hidden clique" problem, a simple prototypical example with a surprisingly rich structure. We also discuss various "combinatorial" testing problems and their connections to high-dimensional random geometric graphs. Time permitting, we study the problem of estimating the mean of a random variable.

Lecture on Combinatorial Statistics

Abstract: In these lectures we discuss some statistical problems with an interesting combinatorial structure behind. We start by reviewing the "hidden clique" problem, a simple prototypical example with a surprisingly rich structure. We also discuss various "combinatorial" testing problems and their connections to high-dimensional random geometric graphs. Time permitting, we study the problem of estimating the mean of a random variable.

Community health workers (CHWs) are increasingly important to healthcare delivery in many African countries. As CHW programs are being scaled up and integrated into national healthcare delivery systems an important policy question is how to deploy CHWs to various regions, given their differences in disease burden and population density as well as the limited national resources for the program. We develop a modeling framework which describes the Quality-Adjusted Life Year (QALY) impact of a CHW program as a function of a region's disease characteristics, operational environment, and CHW deployment density. Specifically, we use continuous logistical models to capture the travel time constraints of CHWs and a novel continuous time stochastic model to describe individual health progression. We quantify the impact of CHW deployment on public health for a special case and build a heuristic to solve a policy makers CHW deployment problem. Leveraging clinical findings, as well as an extensive dataset featuring time-stamps and GPS information for CHW visits in Ghana, we generate policy recommendations for a representative case. We find that in contrast with current guidelines of a constant ratio of 500 patients to CHW, the optimal deployment of the same total number of CHWs ranges from over 800 patients per CHW – in dense regions with limited disease burden – to under 300 patients per CHW – in rural areas with high disease burden. Our sensitivity analyses indicate that the regional differences in the optimal deployment ratio are more extreme in settings with less resources. In addition, we find that a constant patient-to-CHW deployment would result in highly inequitable health outcomes, as a result of regional heterogeneity in patient density and disease burden. In contrast, optimal CHW deployment results in geographically uninform average QALY outcomes, through prioritizing regions with high disease burden and allowing for longer travel times in regions with low patient densities.

BIO

Jónas Oddur Jónasson is the Class of 1943 Career Development Professor and an Assistant Professor of Operations Management at the MIT Sloan School of Management.

His primary research focus is on improving healthcare delivery in resource-limited settings. In particular, Jónas is interested in developing data-driven models describing the impact of operational implementation of healthcare delivery programs on individual disease progression and public health outcomes. In his research Jónas has collaborated with a number of healthcare delivery organizations, including the Earth Institute, the Clinton Health Access Initiative, Riders for Health, and the London Ambulance Service.

Jónas’ research has been published in Operations Research. He was awarded the 2014 Bonder Scholarship for applied Operations Research in Health Services and received first prize in the 2014 INFORMS Manufacturing & Service Operations Management (MSOM) student paper competition for his work on early infant diagnosis of HIV in resource limited settings. Recently he was awarded a Grand Challenges Exploration grant from the Gates Foundation.

Prior to joining MIT, Jónas received his PhD in Management Science and Operations from London Business School, an MPhil from the University of Cambridge, an MSc from the London School of Economics and Political Science, and his BSc from the University of Iceland.

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester). Plus, enjoy a free pizza lunch!

EVENT DETAILS

Where: ISyE Main Bldg, 2nd Floor Atrium

When: Wednesday, November 7 | 11am - 2pm

What: The session will include:

• Networking opportunities in the ISyE atrium
• Food and refreshments

Please plan on staying for the duration of the event and bring copies of your resume and business cards. Dress is business casual.

REGISTER ONLINE by midnight October 26th and upload your resume to be forwarded to the organization representatives!

EVENT SPONSOR

The event is sponsored through the generosity and support of JP Morgan Chase & Co. and APICS - Atlanta Chapter. APICS is a non-profit educational organization addressing operations management and supply chain management issues, and providing professional development opportunities to its members. APICS Membership is free for full time students. Join today at www.apics.org/join and start networking at local APIC Atlanta events. Also make sure to stop by the APICS table at the event.

Abstract: Matrices of low rank are pervasive in big data, appearing in recommender systems, movie preferences, topic models, medical records, and genomics.

While there is a vast literature on how to exploit low rank structure in these datasets, there is less attention on explaining why low rank structure appears in the first place.

In this talk, we explain the abundance of low rank matrices in big data by proving that certain latent variable models associated to piecewise analytic functions are of log-rank. Any large matrix from such a latent variable model can be approximated, up to a small error, by a low rank matrix.

Armed with this theorem, we show how to use a low rank modeling framework to exploit low rank structure even for datasets that are not numeric, with applications in the social sciences, medicine, retail, and machine learning.

Bike-sharing systems are changing the urban transportation landscape; for example, New York launched the largest bike-sharing system in North America in May 2013, with individual trips exceeding 15 million rides for 2017. We have worked with Citibike, using analytics and optimization to change how they manage the system. Huge rush-hour usage imbalances the system - we answer the following two questions: where should bikes be at the start of a day and how can we mitigate the imbalances that develop?

We will survey the analytics we have employed for the former question, where we developed an approach based on continuous-time Markov chains combined with integer programming models to compute daily stocking levels for the bikes, as well as methods employed for optimizing the capacity of the stations. For the question of mitigating the imbalances that result, we will describe both heuristic methods and approximation algorithms that guide both mid-rush hour and overnight rebalancing, as well as for the positioning of corrals, which have been one of the most effective means of creating adaptive capacity in the system. More recently, we have guided the development of Bike Angels, a program to incentivize users to crowdsource “rebalancing rides”, and we will describe its underlying analytics.

This is joint work with Daniel Freund, Shane Henderson, and Eoin O’Mahony, as well as Hangil Chung, Aaron Ferber, Nanjing Jian, Ashkan Nourozi-Fard, Alice Paul, and David Williamson.

Bio:

David Shmoys obtained his PhD in Computer Science from the University of California at Berkeley in 1984, and held postdoctoral positions at MSRI in Berkeley and Harvard University, and a faculty position at MIT before joining the faculty at Cornell University.  He was Chair of the Cornell Provost’s “Radical Collaborations” Task Force on Data Science, co-Chair of the Academic Planning Committee for Cornell Tech, and is Associate Director of the Institute of Computational Sustainability at Cornell University.

David’s research has focused on the design and analysis of efficient algorithms for discrete optimization problems, with applications including scheduling, inventory theory, computational biology, computational sustainability, and most recently, data-driven decision-making in the sharing economy. His work has highlighted the central role that linear programming plays in the design of approximation algorithms for NP-hard problems; his book (co-authored with David Williamson), The Design of Approximation Algorithms, was awarded the 2013 Lanchester Prize by INFORMS.

David is a Fellow of the ACM, INFORMS, and of SIAM, was an NSF Presidential Young Investigator, and has served on numerous editorial boards, currently including Operations Research (co-Area Editor for Optimization) and Mathematics of Operations Research, as well as previous stints on the boards of ORSA Journal of Computing, Mathematical Programming, and the SIAM Journals of both Computing and Discrete Mathematics, where for the latter he also served as Editor-in-Chief.

include gradient, proximal-gradient, (proximal) method of multipliers, alternating

minimization, PDHG, Chambolle Pock, Condat-Vu, (standard, proximal, and

linearized) ADMM, and PD3O. Finite-sum and block-coordinate-friendly properties

are used to develop parallel and asynchronous methods. However, we will

leave out topics such as line search, Nesterov/heavy-ball accelerations, conditional

gradients, and second-order methods. We will also discuss how to recognize

unbounded, infeasible, and other pathological problems by first-order methods.

Co-author: Ernest K. Ryu and Yanli LI.

BIO:  Wotao Yin
Affiliation: University of California; Los Angeles

Lecture on Combinatorial Statistics

Abstract: In these lectures we discuss some statistical problems with an interesting combinatorial structure behind. We start by reviewing the "hidden clique" problem, a simple prototypical example with a surprisingly rich structure. We also discuss various "combinatorial" testing problems and their connections to high-dimensional random geometric graphs. Time permitting, we study the problem of estimating the mean of a random variable.

About Me:    

Hao Helen Zhang
Department of Mathematics
University of Arizona, Tucson, AZ 85721
Email: hzhang@math.arizona.edu

http://math.arizona.edu/~hzhang/

Abstract: Logistic regression is arguably the most widely used and studied non-linear model in statistics. Classical maximum likelihood theory provides asymptotic distributions for the maximum likelihood estimate (MLE) and the likelihood ratio test (LRT), which are universally used for inference. Our findings reveal, however, when the number of features p and the sample size n both diverge, with the ratio p/n converging to a positive constant, classical results are far from accurate. For a certain class of logistic models, we observe, (1) the MLE is biased, (2) variability of the MLE is much higher than classical results and (3) the LRT is not distributed as a Chi-Squared. We develop a new theory that quantifies the asymptotic bias and variance of the MLE, and characterizes asymptotic distribution of the LRT under certain assumptions on the distribution of the covariates. Empirical results demonstrate that our predictions are extremely accurate in finite samples. These novel predictions depend on the underlying regression coefficients through a single scalar, the overall signal strength, which can be estimated efficiently. This is based on joint work with Emmanuel Candes and Yuxin Chen.

My bio: 

I am a fifth year Ph.D. candidate in the Dept. of Statistics, Stanford University, advised by Prof. Emmanuel Candes. During my Ph.D. I have specialized on the area of high-dimensional statistical inference. 

Jorge Nocedal

Northwestern University

We begin by proposing an optimization algorithm that employs only function values and is able to solve noisy problems in thousands of variables. We then consider the problem of training deep neural networks and note that although most high-dimensional nonconvex optimization problems cannot be solved to optimality, deep neural networks have a benign geometry that allows stochastic optimization methods find acceptable solutions. There are, nevertheless, many open questions concerning the optimization process, including trade-offs between parallelism and the predictive ability of solutions.  We discuss classical and new optimization methods in the light of these observations.

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Bio
Jorge Nocedal is the Walter P. Murphy Professor in the Department of Industrial Engineering and Management Sciences at Northwestern University. His research is in optimization, both deterministic and stochastic, and with emphasis on very large-scale problems.  His current work is driven by applications in machine learning. He is a SIAM Fellow, was awarded the 2012 George B. Dantzig Prize and the 2017 Von Neumann Theory Prize for contributions to theory and algorithms of nonlinear optimization. 

Abstract: 

We study the design of state-dependent control for a closed queueing network model of ridesharing systems. We focus on the dispatch policy, where the platform can choose which vehicle to assign when a customer request comes in, and assume that this is the exclusive control lever available. The vehicle once again becomes available at the destination after dropping the customer. We consider the proportion of dropped demand in steady state as the performance measure.

We propose a family of simple and explicit state-dependent policies called Scaled MaxWeight (SMW) policies and prove that under the complete resource pooling (CRP) condition (analogous to the condition in Hall's marriage theorem), each SMW policy leads to exponential decay of demand-dropping probability as the number of vehicles scales to infinity. We further show that there is an SMW policy that achieves the optimal exponent among all dispatch policies, and analytically specify this policy in terms of the customer request arrival rates for all source-destination pairs. The optimal SMW policy protects structurally under-supplied locations. Simulations show substantial gains from our approach in practically relevant parameter settings.

Joint work with Sid Banerjee and Pengyu Qian.

Bio:
Yash Kanoria is an Associate Professor in Decision, Risk and Operations at Columbia Business School, working primarily on matching markets and the design and operations of marketplaces. Previously, he obtained a BTech from IIT Bombay in 2007, a PhD in Electrical Engineering from Stanford in 2012, and spent a year at Microsoft Research New England during 2012-13 as a Schramm postdoctoral fellow. He received an NSF CAREER Award in 2017, a Simons-Berkeley Research Fellowship in 2015 and an INFORMS JFIG paper competition second prize in 2014. He is a finalist for the 2018 Wagner Prize for Excellence in Operations Research Practice.

ABSTRACT:

Uber is running one of the largest marketplaces in the world that matches millions of riders and drivers together. One of the key features of Uber's marketplace is dynamic pricing, so called surge pricing, that aims to balance the demand for trips and the supply of available cars. The surge pricing also motivates drivers to relocate to parts of the city with higher demand. In this talk, I'll discuss the design of the new generation of surge mechanisms that aim to increase the marketplace efficiency. 

BIO: Hamid Nazerzadeh is a Dean's Associate Professor in Business Administration in the Data Sciences and Operations department at Marshall School of Business, and (by courtesy) the Department of Computer Science, University of Southern California. He is currently a Staff Data Scientist and Techlead in Marketplace Optimization group at Uber, on sabbatical leave from USC.

Prof. Nazerzadeh obtained his Ph.D. in Operations Research from Stanford University and his B.Sc. from Sharif University of Technology and has worked at Microsoft, Yahoo!, Google research labs. His research focuses on mechanism design and optimization algorithms and their applications in operations and monetization of online markets. He is a Department Editor of journal of Management Science. He is the recipient of Yahoo! Ph.D. Student Fellowship Award (2007), Honorable Mention in George Dantzig Dissertation Awards (2009), Google Faculty Research Award (2013 & 2016), Marshall Dean's Award for Research Excellence (2014), and INFORMS Revenue Management and Pricing Section Prize (2014). 

Theoretical Foundation of Deep Learning 2018 Workshop website

ABSTRACT:

The study of combinatorial problems with a submodular objective has attracted much attention in recent years, and is partly motivated by the fact that such problems arise in diverse settings such as combinatorial optimization, algorithmic game theory, economics, machine learning and social networks. This talk will survey some of the continuous approaches used in the last decade for constrained submodular maximization. One approach of particular interest that will be discussed is the continuous greedy algorithm and its applications.

BIO: Roy Schwartz in currently an assistant professor in the Department of Computer Science at the Technion.

His research interests include the design and analysis of algorithms, combinatorial optimization, approximation algorithms, the geometry of metric spaces and its applications, submodular optimization, and randomized algorithms

ABSTRACT:

In accordance with the National Organ Transplant Act (NOTA), which requires the efficient and equitable allocation of donated organs, the United Network for Organ Sharing (UNOS) prioritizes patients on the liver-transplant waiting list within given geographic areas based mainly on their most recently reported health status. Accordingly, UNOS requires patients to update their health status at a frequency that depends on their last reported health status. However, patients may elect to update any time within the required timeframe, which creates opportunities to game the system, leading to information asymmetries between UNOS and the patients on the waiting list. This information asymmetry can be alleviated through more frequent updating requirements, but at the price of an increased update burden (e.g., data collection costs and patient inconvenience).

We propose a model that determines health reporting requirements that simultaneously minimize these two (possibly conflicting) criteria, i.e., inequity due to information asymmetry and update burden. Calibrating the model with clinical data, we examine (1) the degree to which an individual patient can benefit from the flexibility inherent to the current health reporting requirements, and (2) alternative recommendations that dominate the current requirements with respect to the two criteria of interest.

RECEPTION WILL FOLLOW

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester). Plus, enjoy a free pizza lunch!

EVENT DETAILS

Where: ISyE Main Bldg, 2nd Floor Atrium

When: Thursday, September 13 | 11am - 2pm

What: The session will include:

• Networking opportunities in the ISyE atrium
• Food and refreshments

Please plan on staying for the duration of the event and bring copies of your resume and business cards. Dress is business casual.

REGISTER ONLINE by September 3rd and upload your resume to be forwarded to the organization representatives!

EVENT SPONSOR

The event is sponsored through the generosity and support of JP Morgan Chase & Co. and APICS - Atlanta Chapter. APICS is a non-profit educational organization addressing operations management and supply chain management issues, and providing professional development opportunities to its members. APICS Membership is free for full time students. Join today at www.apics.org/join and start networking at local APIC Atlanta events. Also make sure to stop by the APICS table at the event.

ABSTRACT:

We consider matrix and tensor factorization problems where there are both unimodal and rank constraints.  Such methods find application in a variety of problems such as target localization, environmental monitoring, epidemic detection, and medical diagnosis.  We presume that we have incomplete (sparse) and noisy samples of a particular field or image and that our objects of interest have spatial extent and can be modeled as low rank and unimodal:  there is a single strong signal peak and this signal decays as one moves away from the strong signal peak.  By exploiting modern signal processing techniques such as matrix completion and active search methods, we develop a high performance, moderate complexity algorithm for peak detection.  This method is extended to the case of multiple targets via novel matrix factorization and isotonic projection methods.  We further extend the approach to handle multimodal sensor data by exploiting tensor completion methods.  Finally, we show how we can exploit our methods to solve a data clustering problem which is motivated by the application of radio map building.  Radio signals can be described by multiple propagation models. We can transform and compress location-labeled wireless channel measurements into a low-dimensional feature matrix. By analyzing the local peaks of the feature matrix, we can identify the regional propagation laws, which enable the clustering of the data. Theoretical performance bounds derived and properties of key matrices are proven.  The methods are compared against the state of the art on both synthetic and real data sets and shown to offer superior performance with moderate complexity.

BIO: Urbashi Mitra received the B.S. and the M.S. degrees from the University of California at Berkeley and her Ph.D. from Princeton University.  Dr. Mitra is currently the Gordon S. Marshall Professor in Engineering at the University of Southern California with appointments in Electrical Engineering and Computer Science. She is the inaugural Editor-in-Chief for the IEEE Transactions on Molecular, Biological and Multi-scale Communications. She has been a member of the IEEE Information Theory Society's Board of Governors (2002-2007, 2012-2017), the IEEE Signal Processing Society’s Technical Committee on Signal Processing for Communications and Networks (2012-2016), the IEEE Signal Processing Society’s Awards Board (2017-2018), and the Vice Chair of the IEEE Communications Society, Communication Theory Working Group (2017-2018). Dr. Mitra is a Fellow of the IEEE.  She is the recipient of: the 2017 IEEE Women in Communications Engineering Technical Achievement Award, a 2015 UK Royal Academy of Engineering Distinguished Visiting Professorship, a 2015 US Fulbright Scholar Award, a 2015-2016 UK Leverhulme Trust Visiting Professorship, IEEE Communications Society Distinguished Lecturer, 2012 Globecom Signal Processing for Communications Symposium Best Paper Award, 2012 US National Academy of Engineering Lillian Gilbreth Lectureship, the 2009 DCOSS Applications & Systems Best Paper Award, Texas Instruments Visiting Professorship (Fall 2002, Rice University), 2001 Okawa Foundation Award, 2000 Ohio State University’s College of Engineering Lumley Award for Research, 1997 Ohio State University’s College of Engineering MacQuigg Award for Teaching, and a 1996 National Science Foundation CAREER Award.  She has been an Associate Editor for the following IEEE publications: Transactions on Signal Processing, Transactions on Information Theory, Journal of Oceanic Engineering, and Transactions on Communications.  Dr. Mitra has held visiting appointments at: King’s College, London, Imperial College, the Delft University of Technology, Stanford University, Rice University, and the Eurecom Institute. Her research interests are in: wireless communications, communication and sensor networks, biological communication systems, detection and estimation and the interface of communication, sensing and control.  

This accelerated learning boot camp covers 2.5 days of instructor lead facilitation and is designed to cover key learning points of the CSCP learning system. The learning system content is included in the course. This boot camp is beneficial for the self-study learner planning to take the APICS CSCP certification exam.

The APICS Certified Supply Chain Professional™ (CSCP) program takes a broad view of the field, extending beyond internal operations to encompass all the steps throughout the supply chain--from the supplier, through the company, to the end consumer--and how to effectively manage the integration of these activities to maximize the company’s value chain.

The CSCP program provides candidates with the necessary tools to effectively manage global supply chain activities and enables them to implement best practice approaches to increase supply chain efficiencies.

WHO SHOULD ATTEND

The APICS CSCP is designed for supply chain professionals and people managing and planning extended supply chains especially people in:

• Supply chain design
• Supplier management
• Transportation
• Supply chain management
• Distribution channels
• 3PL, 4PL management
• Customer management
• Supply chain consulting

HOW YOU WILL BENEFIT

• Master advanced supply chain management principles that extend beyond an organization’s internal end-to-end operations from suppliers to customers
• Apply supply chain knowledge and analytical skills to streamline operations and produce bottom line results
• Explain the role of each element of the integrated supply chain concept
• Understand how successful supply chain management adds value to your organization
• Learn how to develop a supply chain strategy that aligns with corporate strategy
• Understand natural dynamics within the supply chain to optimize performance and profitability
• Evaluate the process constraints and choices within Global Logistics to establish a plan linked to overall strategy
• Effectively use customer data to improve service performance and increase value to suppliers and customers
• Understand the strategic importance of purchasing and supply relationships
• Understand the innovative technologies enabling collaborative commerce and global visibility
• Apply technology to enhance performance of distribution, reverse logistics, and global supply chain communications

WHAT IS COVERED

Supply Chain Design

• Addresses the concepts and strategies used for developing a supply chain strategy aligning with business goals and corporate strategy
• Designing a supply chain for the flow of product, information and cash
• Understanding and using the SCORreg model

Supply Chain Planning and Execution

• Addresses the processes required to procure and deliver goods and services
• Management of demand and supply relationships

Supply Chain Planning and Execution

• Related concepts of order management and customer service
• Defining and measuring logistics customer service outputs

Supply Chain Improvement and Best Practices

• Managing and balancing supply and demand through measuring, analyzing and improving supply chain processes
• Compliance with standards and regulations
• The importance of sustainable best practices and social responsibility
• Assessment of risk within the supply chain

COURSE MATERIALS

Participants will be provided the APICS CSCP course reading material and one-year access to the APICS online courseware.

COURSE PREREQUISITES

None.

PROGRAM TIMES

On the first day, please check in at least 30 minutes before the class start time.

• First Day - 1pm to 5:00pm (lunch not included)
• ​Second Day - 8:00am to 5:00pm
• Third Day - 8:00am to 5:00pm

This accelerated learning boot camp covers 2.5 days of instructor lead facilitation and is designed to cover key learning points of the CLTD learning system. The learning system content is included in the course. This boot camp is intended for the self-study learner planning to take the APICS CLTD certification exam.

The APICS Certified in Logistics, Transportation and Distribution™ (CLTD) certification provides you with the information you need to meet today’s supply chain logistics challenges.

Created by APICS and a team of subject matter experts, the CLTD designation covers a comprehensive body of knowledge that sets the global standard for best practices in logistics, transportation and distribution.

WHO SHOULD ATTEND

The APICS CLTD is designed for logistics professionals and people planning and managing global logistics across all industries, especially:

• Logistics managers/engineers
• Supply chain managers
• Traffic managers
• Transportation/fleet managers
• Warehouse operations/distribution managers
• Reverse logistics managers
• Supply chain/operations consultants

No matter where you are in your career, earning the CLTD credential will demonstrate your mastery of a broad range of logistic, transportation and distribution topics.

HOW YOU WILL BENEFIT

• Master the essential knowledge needed for the logistics, transportation and distribution industry
• Bring new ideas to your organization
• Remain current with global logistics trends and developments
• Boost your confidence with recognition as a logistics expert
• Reduce costs and impact your organization’s bottom line
• Allow you to more effectively communicate with others across your extended supply chain

Participants will gain an understanding of the processes, best practices, and tools used by today’s logistics managers and how to design and administer systems to control the movement and positioning of material and information flows to satisfy customer requirements at the lowest total cost.

WHAT IS COVERED

Logistics and Supply Chain Overview

• Overview of macro and micro logistics processes and systems defining material and information flows
• Achieving consistent customer service at the lowest total cost
• Illustrate logistics cross-functional relationships with marketing, finance and manufacturing

Capacity Planning and Demand Management

• Understand strategic and operational considerations, and important trade-offs related to logistics, procurement, manufacturing and customer relationship management
• Developing resource-alignment and organizational forecasts

Order Management

• Related concepts of order management and customer service
• Defining and measuring logistics customer service outputs

Inventory and Warehouse Management

• Review methods and techniques firms use to control, manage, and store inventory
• Understand economic and service benefits warehousing delivers
• Define warehouse types and operating characteristics

Transportation

• Understand major principles and processes of transportation management
• Review fundamental mode roles, capabilities and responsibilities

Global Logistics Considerations

• Navigating today’s more complex supply chains from a total cost perspective
• Using transportation modal analysis, security considerations and regulations

Logistics Network Design

• Understand how transportation and inventory economics impact network design considerations
• Introduction to modeling tools and techniques
• Working with the processes, resources and tools for managing risk

Reverse Logistics Considerations

• A look at the expanded view of reverse supply chain flows and sustainability
• What is logistics social responsibility and the triple bottom line

COURSE MATERIALS

Participants will be provided the APICS CLTD course reading material and one-year access to the APICS online courseware.

COURSE PREREQUISITES

None.

PROGRAM TIMES

On the first day, please check in at least 30 minutes before the class start time.

• First Day - 8:00am to 5:00pm
• ​Second Day - 8:00am to 5:00pm
• Third Day - 8:00am to 12:00pm (lunch not included)

The 2018 Savannah Logistics Lunch will be held on Friday, July 20. Presented by the Center of Innovation for Logistics and HunterMaclean, the fourth annual Savannah Logistics Lunch will explore the regional opportunities and challenges of Managing More for the Future: Managing infrastructure, technology, and workforce for more cargo, transparency, and expectations.

WHEN:

Friday, July 20th, 2018
11:30 a.m. – 1:15 p.m.
Doors open at 10:30 a.m. Program begins promptly at 11:30 a.m.

WHERE:

Savannah International Trade & Convention Center
1 International Drive, Savannah, GA 31421

REGISTRATION:

Registration is $35.00 and is available online at https://tinyurl.com/SLL18. Tickets must be purchased in advance, as seats are limited.

Our free, one-hour Webinar, "Three Key Principles of Influence: Driving Business Deals and Effective Negotiations", will provide an overview on how to better strengthen preparation, planning, and execution activities involved with both simple and complex negotiations. High-level industry-based negotiation techniques and tools that can be used on the job or at home will be discussed, as well as process and organizational tips you can count on to help you drive more effective negotiations for better business deals.

WHO SHOULD ATTEND

This webinar is ideal for procurement, supply chain, operations, facilities, finance, and supplier diversity executives as well as professionals with category leadership, contract management or supplier performance responsibilities. It should also appeal to and support negotiation skill development of sales executives, project managers, team leaders, entrepreneurs and small business executives.

HOW YOU WILL BENEFIT

• Increase your awareness of popular negotiation prep tools and philosophies
• Deepen your understanding of key universal contract terms to better prepare your approach
• Learn to better manage the use of techniques and soft skills to direct and redirect negotiation momentum
• Leverage your Personal Negotiation Style to comfortably control negotiations and lead your support team

ABOUT THE PRESENTER

G’Sean Williams is a Lead Instructor for the Georgia Tech Supply Chain & Logistics Institute's Procurement & Supply Management (PSM) Leadership Certificate courses where he uses his experience in negotiating deals, managing contracts and leading procurement teams globally for companies like Intel, WestRock, and Bristol Myers Squibb. He is also Executive Director of Supply Chain Practice for SMS Advisors, a Procurement & Supply Chain Specialty Consulting Firm for Small Business Suppliers and Corporations. Mr. Williams, CPSM, serves as the Chair of the Board for Institute of Supply Management Carolinas-Virginia (ISM-CV).

PSM COURSE SERIES

Offered September 24-28, 2018 in Atlanta, GA. Learn more at https://www.scl.gatech.edu/PSM.

This event supports active duty service members and military spouses enrolled in the Veterans Education Training and Transition (VET2) - Lean Six Sigma Black Belt, Supply Chain Project Management and Telecommunications programs.

All participating service members and spouses will earn their certificate of training and transition to the civilian workforce within 30 to 120 days of attending this event.

Tentative Agenda

• 0800-0830 – Organization Set Up
• 0830-0835 – Welcome
• 0835-1030 – Meet the attending students/find your newest employee

Please RSVP online or e-mail/call Tammy Benbow (tammy.benbow@pe.gatech.edu/    (912)964-3816) if you are interested in attending/representing your organization at the event.

With June 11th right around the corner, please RSVP ASAP. Space is limited.

Automotive Logistics Session 5: Securing the goods
Wednesday 23rd May, 2018 | 9:00 – 10:15

The primary job of automotive logistics operators is and will remain serving plants, partners, dealers and customers at the right time, with the right quality and at the right cost. However, complex technologies, regulations and locations add new risk and requirements, and manufacturers and logistics providers must look to new processes and tools to keep their supply chains safe.

This session will consider:

• How new technology can help inbound and service parts supply chains
• Cyber security risks and strategies
• What can be done in terms of the data to help reduce damage and cost levels

Take this opportunity to refocus on your core rationale and contemplate the role IT could play in helping with the most fundamental logistics activities of all.

Adam Pieniazek Sr. Manager Logistics North America Magna Powertrain
Benoit Montreuil Director, Supply Chain & Logistics Institute Georgia Tech
Dustin Barney Director, Logistics and Materials Management AGCO
Ed Nabrotzky Chief Solutions Officer Omni ID

Session 6: The Decision Makers (joint session with automotiveIT International conference)
Wednesday 23rd May, 2018 | 11:00 – 12:15

Information and communication technologies are redefining the auto industry. But who is driving the changes and managing the transformation inside companies? How should the industry collaborate to find new recruits, train staff and work with new players and startups? Key executives and academics discuss the respective roles of different departments and stakeholders on leading digital transformation across all layers of manufacturing and the supply chain.

Join the discussion to learn:

• How automotive IT and supply chain departments are adjusting to their role in the digital era
• How CIOs, CDOs and other department heads work together to digitalize their operations
• The biggest IT and supply chain challenges and opportunities automakers and suppliers face
• The role of universities, training and startups in advancing – and disrupting – the industry

Paulo Monteiro Inbound Logistics Manager Volkswagen Group of America
Benoit Montreuil Director, Supply Chain & Logistics Institute Georgia Tech
Shoukat Ali Bhamani VP, Chief Information Officer & Chief Digital Officer Schaeffler Group Americas
Tom Roach Director, North American Information Technology Porsche Cars North America

Demo Day 

Transparency18’s Demo Day showcases emerging, disruptive and innovative technology solutions. Participants should be prepared to showcase emerging, disruptive, and innovative technology solutions catapulting freight transportation and logistics to the next level. There will be NO SLIDES! We repeat, NO SLIDES. The format will be fast-paced, 7-minute demonstrations providing live, simulated or video of new technology products and services designed and created for:

• Blockchain
• Internet of Things – Transportation (IoTT)
• Supply chain & logistics efficiency and optimization
• Semi-autonomous
• Data optimization
• Driver efficiency
• Fuel efficiency
• Tracking and visibility
• Driver management
• Truck and trailer efficiency

Important Dates

• Demo Day applications will be accepted through Monday, March 9, 2018
• 25 Demo Day presenters will be announced on March 31, 2018
• All presenters are required to be available for a run through on Monday, May 21, and to present on Tuesday, May 22, 2018

How to Prepare

• Selection criteria will be based upon product relevance to the industry and timeliness (to market in less than 6 months)
• Focus on emerging, disruptive, and innovative technology solutions
• Timed 7-minute presentations, no slide decks

What’s Provided

• Break out area where presenters will have product demo/networking stations set up to share with attendees when not on stage
• High-top tables with power access
• Presentations professionally recorded and posted to the event website for post event viewing
• Press hour access for presenting companies to meet with media attendees

In partnership with Freightwaves, the Georgia Tech Supply Chain & Logistics Institute will be co-hosting Transparency18.

ABSTRACT: Operations research has had and continues to have significant impact in almost every aspect of the aerospace enterprise – from preliminary design of aircraft to the setting of airline ticket prices. I have been drawn to the operational side of the aerospace enterprise and to the development and use of OR in aerospace operations because of my passion for making sure that the things that fly do so as efficiently as possible. To this end I will highlight the ways I have used modeling, simulation, and optimization to solve some very challenging problems in aircraft, airline, airport, and air traffic operations. With respect to aircraft operations, I will discuss the development of noise and fuel optimal arrival procedures that have already yielded significant savings throughout the country, and the development of a stochastic model of en route winds that led to a patented methodology for determining the cruise speed that guarantees compliance with a specified arrival time while minimizing expected fuel burn during flight. With regards to airline operation, I will discuss the development of robust aircraft flight schedules (the strategic aspect of airline operations) and the development of an algorithm for airline schedule recovery that jointly (as opposed to sequentially) considers passengers, crew, and aircraft when determining how to respond to schedule disruptions (the tactical aspect of airline operations). With regards to airport operations, I will discuss the development of robust gating assignment algorithms that reduce congestion on the ramp and the need for the last-minute re-assignment of flights to gates when aircraft are delayed. With regards to air traffic operations, I will discuss the development of runway and terminal area scheduling algorithms that are robust to uncertainties in aircraft pushback and terminal area arrival times, and provide significant time and fuel savings through the optimal assignment of flights to, and scheduling of flight at runways and terminal area entry and exit fixes.

BIO: John-Paul Clarke is a College of Engineering Dean’s Professor at Georgia Tech, where he has an appointment in Aerospace Engineering and a courtesy appointment in Industrial and Systems Engineering, and serves as Director of the Air Transportation Laboratory.

Dr. Clarke’s research in aircraft trajectory prediction and optimization, especially as it pertains to the development of flight procedures that reduce the environmental impact of aviation, has been instrumental in changing both the theory and the practice of flight procedure design. More broadly, his research focuses on the development and use of stochastic models and optimization algorithms to improve the efficiency and robustness of aircraft, airline, airport, and air traffic operations.

Professor Clarke was co-Chair of the National Academies Committee that developed the US National Agenda for Autonomy Research related to Civil Aviation, and a member of the National Academies Committee that reviewed the Next Generation Air Transportation System. He is currently co-Chair of the Joint Planning Committee for the AIAA-AAAF Aviation Noise and Emissions Reduction Symposium (ANERS) and a member of the NASA Advisory Council Aeronautics Committee. Over the years, he has chaired or served on advisory and technical committees chartered by the AIAA, EU, FAA, ICAO, NASA, the National Academies, the US Army, and the US DOT.

Dr. Clarke received the S.B., S.M., and Sc.D. degrees from the Massachusetts Institute of Technology (MIT) in 1991, 1992, and 1997, respectively. His many prior honors include the 1999 AIAA/AAAE/ACC Jay Hollingsworth Speas Airport Award, the 2003 FAA Excellence in Aviation Award, the 2006 National Academy of Engineering Gilbreth Lectureship, the 2012 AIAA/SAE William Littlewood Lectureship, and the SAE Environmental Excellence in Transportation Award in 2015. He is a Fellow of the AIAA, and is a member of AGIFORS, INFORMS, and Sigma Xi.

This accelerated learning boot camp covers 2.5 days of instructor lead facilitation and is designed to cover key learning points of the CLTD learning system. The learning system content is included in the course. This boot camp is intended for the self-study learner planning to take the APICS CLTD certification exam.

The APICS Certified in Logistics, Transportation and Distribution™ (CLTD) certification provides you with the information you need to meet today’s supply chain logistics challenges.

Created by APICS and a team of subject matter experts, the CLTD designation covers a comprehensive body of knowledge that sets the global standard for best practices in logistics, transportation and distribution.

WHO SHOULD ATTEND

The APICS CLTD is designed for logistics professionals and people planning and managing global logistics across all industries, especially:

• Logistics managers/engineers
• Supply chain managers
• Traffic managers
• Transportation/fleet managers
• Warehouse operations/distribution managers
• Reverse logistics managers
• Supply chain/operations consultants

No matter where you are in your career, earning the CLTD credential will demonstrate your mastery of a broad range of logistic, transportation and distribution topics.

HOW YOU WILL BENEFIT

• Master the essential knowledge needed for the logistics, transportation and distribution industry
• Bring new ideas to your organization
• Remain current with global logistics trends and developments
• Boost your confidence with recognition as a logistics expert
• Reduce costs and impact your organization’s bottom line
• Allow you to more effectively communicate with others across your extended supply chain

Participants will gain an understanding of the processes, best practices, and tools used by today’s logistics managers and how to design and administer systems to control the movement and positioning of material and information flows to satisfy customer requirements at the lowest total cost.

WHAT IS COVERED

Logistics and Supply Chain Overview

• Overview of macro and micro logistics processes and systems defining material and information flows
• Achieving consistent customer service at the lowest total cost
• Illustrate logistics cross-functional relationships with marketing, finance and manufacturing

Capacity Planning and Demand Management

• Understand strategic and operational considerations, and important trade-offs related to logistics, procurement, manufacturing and customer relationship management
• Developing resource-alignment and organizational forecasts

Order Management

• Related concepts of order management and customer service
• Defining and measuring logistics customer service outputs

Inventory and Warehouse Management

• Review methods and techniques firms use to control, manage, and store inventory
• Understand economic and service benefits warehousing delivers
• Define warehouse types and operating characteristics

Transportation

• Understand major principles and processes of transportation management
• Review fundamental mode roles, capabilities and responsibilities

Global Logistics Considerations

• Navigating today’s more complex supply chains from a total cost perspective
• Using transportation modal analysis, security considerations and regulations

Logistics Network Design

• Understand how transportation and inventory economics impact network design considerations
• Introduction to modeling tools and techniques
• Working with the processes, resources and tools for managing risk

Reverse Logistics Considerations

• A look at the expanded view of reverse supply chain flows and sustainability
• What is logistics social responsibility and the triple bottom line

COURSE MATERIALS

Participants will be provided the APICS CLTD course reading material and one-year access to the APICS online courseware.

COURSE PREREQUISITES

None.

PROGRAM TIMES

On the first day, please check in at least 30 minutes before the class start time.

• First Day - 8:00am to 5:00pm
• ​Second Day - 8:00am to 5:00pm
• Third Day - 8:00am to 12:00pm (lunch not included)

This accelerated learning boot camp covers 2.5 days of instructor lead facilitation and is designed to cover key learning points of the CSCP learning system. The learning system content is included in the course. This boot camp is beneficial for the self-study learner planning to take the APICS CSCP certification exam.

The APICS Certified Supply Chain Professional™ (CSCP) program takes a broad view of the field, extending beyond internal operations to encompass all the steps throughout the supply chain--from the supplier, through the company, to the end consumer--and how to effectively manage the integration of these activities to maximize the company’s value chain.

The CSCP program provides candidates with the necessary tools to effectively manage global supply chain activities and enables them to implement best practice approaches to increase supply chain efficiencies.

WHO SHOULD ATTEND

The APICS CSCP is designed for supply chain professionals and people managing and planning extended supply chains especially people in:

• Supply chain design
• Supplier management
• Transportation
• Supply chain management
• Distribution channels
• 3PL, 4PL management
• Customer management
• Supply chain consulting

HOW YOU WILL BENEFIT

• Master advanced supply chain management principles that extend beyond an organization’s internal end-to-end operations from suppliers to customers
• Apply supply chain knowledge and analytical skills to streamline operations and produce bottom line results
• Explain the role of each element of the integrated supply chain concept
• Understand how successful supply chain management adds value to your organization
• Learn how to develop a supply chain strategy that aligns with corporate strategy
• Understand natural dynamics within the supply chain to optimize performance and profitability
• Evaluate the process constraints and choices within Global Logistics to establish a plan linked to overall strategy
• Effectively use customer data to improve service performance and increase value to suppliers and customers
• Understand the strategic importance of purchasing and supply relationships
• Understand the innovative technologies enabling collaborative commerce and global visibility
• Apply technology to enhance performance of distribution, reverse logistics, and global supply chain communications

WHAT IS COVERED

Supply Chain Design

• Addresses the concepts and strategies used for developing a supply chain strategy aligning with business goals and corporate strategy
• Designing a supply chain for the flow of product, information and cash
• Understanding and using the SCORreg model

Supply Chain Planning and Execution

• Addresses the processes required to procure and deliver goods and services
• Management of demand and supply relationships

Supply Chain Planning and Execution

• Related concepts of order management and customer service
• Defining and measuring logistics customer service outputs

Supply Chain Improvement and Best Practices

• Managing and balancing supply and demand through measuring, analyzing and improving supply chain processes
• Compliance with standards and regulations
• The importance of sustainable best practices and social responsibility
• Assessment of risk within the supply chain

COURSE MATERIALS

Participants will be provided the APICS CSCP course reading material and one-year access to the APICS online courseware.

COURSE PREREQUISITES

None.

PROGRAM TIMES

On the first day, please check in at least 30 minutes before the class start time.

• First Day - 1pm to 5:00pm (lunch not included)
• ​Second Day - 8:00am to 5:00pm
• Third Day - 8:00am to 5:00pm

Qie He, UMN

ABSTRACT:

This talk consists of two parts. The first part focuses on speed optimization. Optimizing truck speed significantly reduces energy consumption and greenhouse gas emissions, and becomes viable recently with the arrival of self-driving trucks. We study two models for speed optimization: speed optimization over a fixed route and the joint routing and speed optimization problem over a network. We derive fast algorithms for both problems to obtain provably optimal solutions. In the second part of the talk, we introduce a model that aims to find vehicle routes and service schedules simultaneously to minimize the total transportation and customers' inconvenience cost, where each customer's inconvenience cost is modeled as a convex function of the service start time at that customer. We are able to extend our algorithm derived for the joint routing and speed optimization problem to this new model. Our algorithms significantly outperform state-of-the-art optimization software for all proposed models, and provide an opportunity for fleet managers to optimize fleet operations in real time.

BIO: Dr. Qie He is an assistant professor in the Department of Industrial and Systems Engineering, University of Minnesota. He obtained his PhD degree in Industrial Engineering in 2013 from the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech, working with Prof. Shabbir Ahmed and Prof. George Nemhauser. His main research interests are to solve large scale planning and dynamic operational problems in transportation and healthcare, with a methodological focus on optimization (integer and combinatorial optimization, global optimization, stochastic programming, etc.).

Praneeth Netrapalli, Microsoft

ABSTRACT:

Non-convex optimization is ubiquitous in modern machine learning applications. Gradient descent based algorithms (such as gradient descent or Nesterov's accelerated gradient descent) are widely used in practice since they have been observed to perform well on these problems. From a theoretical standpoint, however, this is quite surprising since these nonconvex problems are teeming with highly suboptimal saddle points, and it is well known that gradient descent (and variants) can get stuck in such saddle points. A key question that arises in resolving this apparent paradox is whether gradient descent based algorithms escape saddle points where the Hessian has negative eigenvalues and converge to points where Hessian is positive semidefinite (such points are called second-order stationary points) efficiently. We answer this question in the affirmative by showing the following: (1) Perturbed gradient descent converges to second-order stationary points in a number of iterations which depends only poly-logarithmically on the dimension (i.e., it is almost “dimension-free”). The convergence rate of this procedure matches the well-known convergence rate of gradient descent to first-order stationary points, up to log factors, and (2) A variant of Nesterov’s accelerated gradient descent converges to second-order stationary points at a faster rate than perturbed gradient descent.
 
The key technical components behind these results are (1) understanding geometry around saddle points and (2) designing a potential function for Nesterov’s accelerated gradient descent for non-convex problems.
 
Based on joint works with Chi Jin, Rong Ge, Sham M. Kakade and Mike Jordan.

BIO: Praneeth Netrapalli is a researcher at Microsoft Research India, Bengaluru since August 2016. Prior to this, he was a postdoctoral researcher at Microsoft Research New England in Cambridge, MA. He obtained MS and PhD in ECE from UT Austin advised by Sujay Sanghavi. Before coming to UT, he worked in the Quantitative Analysis group at Goldman Sachs, Bangalore. He obtained B-Tech in Electrical Engineering from IIT Bombay. His research focuses on designing efficient algorithms for machine learning problems primarily via stochastic and nonconvex optimization.

ABSTRACT: We consider the robust submodular maximization problem subject to a matroid constraint in the offline as well as online setting. In the offline version we are given a collection of k monotone submodular functions and a matroid on a ground set of size $n$. The goal is to select one independent set that maximizes the minimum of the submodular functions. This problem is known to be NP-hard to approximate to any polynomial factor. We design a  bi-criteria approximation algorithm that returns a set S with (nearly) optimal value and such that it is the union of few independent sets. This result improves on the previous ones known for uniform matroids or the general matroid case when $k$ is constant. Our result also implies similar bi-criteria approximation for the knapsack constraint as well as multiple matroid constraints. We also note that no bi-criteria approximation algorithm is possible for non-monotone submodular functions in contrast to the setting of a single submodular function.
In the online version of the problem, we receive a new collection of functions at each time step and aim to pick an independent set in every stage. We measure the performance of the algorithm in the regret setting where the goal is to give a solution that compares well to picking a single set for all stages. Again, we give a bi-criteria approximation algorithm which has a (nearly) optimal approximation as well as sub-linear regret bounds.

Speaker: Derya Demirtas

https://www.isye.gatech.edu

https://chhs.gatech.edu

Public access defibrillator location under demand and supply uncertainty

Abstract

Out-of-hospital cardiac arrest (OHCA) is a significant public health issue, and treatment, namely, cardiopulmonary resuscitation (CPR) and defibrillation, is very time sensitive. Public access defibrillation programs, which deploy automated external defibrillators (AEDs) for lay responder use in an emergency, reduce the time to defibrillation and improve survival rates. In the first part of the talk, I will present the models we developed to guide the deployment of AEDs in public settings. Our models generalize the maximal covering location problem and are motivated by real-world views on AED retrieval behavior by lay responders during a cardiac arrest emergency. We formulated three mixed integer nonlinear models and derived either equivalent integer linear reformulations or easily computable bounds. A case study where we apply our modeling framework to data from Toronto, Canada will also be presented. In the second part of the talk, I will talk about an ongoing work on a brand-new location problem where both demand (OHCA) and supply (AED and lay responder) locations are subject to uncertainty. This new problem is inspired by a Dutch civilian response smart phone app which alerts volunteers when an OHCA occurs in the vicinity and directs them to first a nearby AED and then to the victim. I will propose a comprehensive modeling framework which combines spatial data analytics, optimization and geographical information systems (GIS) modeling. This framework is primarily motivated by the AED deployment in the Netherlands but can be extended to various other settings in which demand and supply locations are uncertain, such as emergency planning and humanitarian logistics.

Bio

Derya Demirtas is a visiting faculty in the Industrial and Systems Engineering and in the Center for Health & Humanitarian Systems at Georgia Tech. She is an assistant professor in the Department of Industrial Engineering and Business Information Systems at the University of Twente, the Netherlands. She received her BSc in Industrial Engineering with a double major in Computer Engineering from the Middle East Technical University, Turkey (2008). She then obtained her MMath in Combinatorics & Optimization from the University of Waterloo, Canada (2010), and her PhD in Industrial Engineering from the University of Toronto, Canada (2016). She received the second place in the INFORMS Section on Public Sector Operations Research (PSOR) best paper competition in 2012. She was recognized by the American Heart Association with the Young Investigator Award in 2015, and won the best poster abstract presentation award by the National Association of EMS Physicians (NAEMSP) in 2016.

ABSTRACT:

Out-of-hospital cardiac arrest (OHCA) is a significant public health issue, and treatment, namely, cardiopulmonary resuscitation (CPR) and defibrillation, is very time sensitive. Public access defibrillation programs, which deploy automated external defibrillators (AEDs) for lay responder use in an emergency, reduce the time to defibrillation and improve survival rates. In the first part of the talk, I will present the models we developed to guide the deployment of AEDs in public settings. Our models generalize the maximal covering location problem and are motivated by real-world views on AED retrieval behavior by lay responders during a cardiac arrest emergency. We formulated three mixed integer nonlinear models and derived either equivalent integer linear reformulations or easily computable bounds. A case study where we apply our modeling framework to data from Toronto, Canada will also be presented. In the second part of the talk, I will talk about an ongoing work on a brand-new location problem where both demand (OHCA) and supply (AED and lay responder) locations are subject to uncertainty. This new problem is inspired by a Dutch civilian response smart phone app which alerts volunteers when an OHCA occurs in the vicinity and directs them to first a nearby AED and then to the victim. I will propose a comprehensive modeling framework which combines spatial data analytics, optimization and geographical information systems (GIS) modeling. This framework is primarily motivated by the AED deployment in the Netherlands but can be extended to various other settings in which demand and supply locations are uncertain, such as emergency planning and humanitarian logistics.

BIO:  Derya Demirtas is a visiting faculty in the Industrial and Systems Engineering and in the Center for Health & Humanitarian Systems at Georgia Tech. She is an assistant professor in the Department of Industrial Engineering and Business Information Systems at the University of Twente, the Netherlands. She received her BSc in Industrial Engineering with a double major in Computer Engineering from the Middle East Technical University, Turkey (2008). She then obtained her MMath in Combinatorics & Optimization from the University of Waterloo, Canada (2010), and her PhD in Industrial Engineering from the University of Toronto, Canada (2016). She received the second place in the INFORMS Section on Public Sector Operations Research (PSOR) best paper competition in 2012. She was recognized by the American Heart Association with the Young Investigator Award in 2015, and won the best poster abstract presentation award by the National Association of EMS Physicians (NAEMSP) in 2016.

ABSTRACT: 

 "We bring the tools from Blackwell’s seminal result on comparing two stochastic experiments from 1953, to shine a new light on a modern  application of great interest: Generative Adversarial Networks (GAN). Binary hypothesis testing is at the center of training GANs, where a trained neural network (called a critic) determines whether a given sample is from the real data or the generated (fake) data. By jointly training the generator and the critic, the hope is that eventually the trained generator will generate realistic samples. One of the major challenges in GAN is known as “mode collapse”; the lack of diversity in the samples generated by thus trained generators. We propose a new training framework, where the critic is fed with multiple samples jointly (which we call packing), as opposed to each sample separately as done in standard GAN training. With this simple but fundamental departure from existing GANs, experimental results show that the diversity of the generated samples improve significantly. We analyze  this practical gain by first providing a formal mathematical definition of mode collapse and making a fundamental connection between the idea of packing and the intensity of mode collapse. Precisely, we show that the packed critic naturally penalizes mode collapse, thus encouraging generators with less mode collapse. The analyses critically rely on operational interpretation of hypothesis testing and corresponding data processing inequalities, which lead to sharp analyses with simple proofs. For this talk, I will assume no prior background on GANs"

BIO: Sewoong Oh is an Assistant Professor of Industrial and Enterprise Systems Engineering at UIUC. He received his PhD from the department of Electrical Engineering at Stanford University. Following his PhD, he worked as a postdoctoral researcher at Laboratory for Information and Decision Systems (LIDS) at MIT. His research interest is in theoretical machine learning, including spectral methods, ranking, crowdsourcing, estimation of information measures, differential privacy, and generative adversarial networks. He was co-awarded the best paper award at the SIGMETRICS in 2015, NSF CAREER award in 2016, SIGMETRICS rising star award in 2017, and GOOGLE Faculty Research Award. 

ABSTRACT:

Queueing theory has produced an extensive set of limit theorems that provide insight into the behavior of service systems in various asymptotic regimes. One key insight from this literature has been largely ignored over the years. In particular, the theory also identifies the key time scales and statistical parameters of the traffic that affect performance in these regimes. This suggests that statistical analysis of such traffic should therefore focus on those time scales and on accurately measuring those statistical parameters. In many real-world settings, this means that one should focus statistical attention on measuring (for example) long time-scale autocorrelation structure, and that fine-scale structure (like marginals of inter-arrival times) can largely be ignored. We illustrate this “top-down” approach in the context of analysis of high-intensity arrivals associated with call center data sets. Our talk will also discuss a suite of statistical tools that we are building that leverage this top-down perspective. These ideas are being developed jointly with Harsha Honnappa, Xiaowei Zhang, and Zeyu Zheng.

BIO:  Peter W. Glynn is the Thomas Ford Professor in the Department of Management Science and Engineering (MS&E) at Stanford University, and also holds a courtesy appointment in the Department of Electrical Engineering. He received his Ph.D in Operations Research from Stanford University in 1982. He then joined the faculty of the University of Wisconsin at Madison, where he held a joint appointment between the Industrial Engineering Department and Mathematics Research Center, and courtesy appointments in Computer Science and Mathematics. In 1987, he returned to Stanford, where he joined the Department of Operations Research. From 1999 to 2005, he served as Deputy Chair of the Department of Management Science and Engineering, and was Director of Stanford's Institute for Computational and Mathematical Engineering from 2006 until 2010. He served as Chair of MS&E from 2011 through 2015. He is a Fellow of INFORMS and a Fellow of the Institute of Mathematical Statistics, and was an IMS Medallion Lecturer in 1995 and INFORMS Markov Lecturer in 2014. He was co-winner of the Outstanding Publication Awards from the INFORMS Simulation Society in 1993, 2008, and 2016, was a co-winner of the Best (Biannual) Publication Award from the INFORMS Applied Probability Society in 2009, and was the co-winner of the John von Neumann Theory Prize from INFORMS in 2010. In 2012, he was elected to the National Academy of Engineering. He was Founding Editor-in-Chief of Stochastic Systems and is currently Editor-in-Chief of Journal of Applied Probability and Advances in Applied Probability. His research interests lie in simulation, computational probability, queueing theory, statistical inference for stochastic processes, and stochastic modeling.

ABSTRACT:

Access to information is of major concern in a wide range of domains including health, education, and labor markets. Word-of-mouth campaigns are an effective tool for disseminating information that can be leveraged to improve people’s lives. Within health, specifically, by targeting individuals with crucial health information that might be relevant to them, we can potentially create an information cascade that reaches a large portion of the population, and in turn, improve health outcomes for many people.
 
In traditional models for word-of-mouth campaigns, the objective has generally been based on reaching as many people as possible. However, a number of studies both within health and in commercial settings have shown that the indiscriminate spread of a product by word-of-mouth can result in overexposure where the information or product reaches a part of the population that is not receptive to it. This can lead to negative reputational effects or decrease the efficacy of future campaigns. In the first part of the talk, we ask how we should make use of social influence when there is a risk of overexposure. We develop and analyze a theoretical model for this process; we show that it captures a number of the qualitative phenomena associated with overexposure and provide an efficient algorithm to find the optimal campaigning strategy.
 
In the second part of the talk, we focus on the stark inequalities in the availability of health-related data. We focus on the lack of data on health information needs of individuals in Africa and explore the role that search engines can play in uncovering people’s everyday health information needs, concerns, and misconceptions. We analyze Bing searches related to HIV/AIDS in all 54 nations in Africa, and show that these expose a wide range of themes that shed light on how health information needs vary geographically as well as by demographic groups. We discuss the potential for these results to inform targeted education efforts to decrease the burden of the disease.

BIO: Rediet Abebe is a Ph.D. candidate in Computer Science at Cornell. Her research focuses on algorithms, artificial intelligence, and applications to social good. Her work has generously been supported by fellowships and scholarships through Facebook (2017-2019), Google (2016-2017), and the Cornell Graduate School (2015-2016). She is also a 2013-2014 Harvard Cambridge Scholar.

OVERVIEW

Our free, one-hour webinar will provide an overview of the processes recommended by the Project Management Institute (PMI) to complete a complex supply chain project using the methodology outlined their official publication, the Project Management Book of Knowledge (PMBOK). To provide a high-level illustration of these process steps in action, SCL course instructor and webinar host John Szoke with LeanCor, will provide a review of a warehouse design project that was executed in 2017 using the PMBOK methodology coupled with lean principles. Attendees will be able to have their questions answered by a Project Management expert!

WHO SHOULD ATTEND

Logistics professionals responsible for managing projects that relate to supply chain strategy, network analysis, facility design build, supply chain technology selection and implementation, and continuous process improvement initiatives.

ABOUT THE PRESENTER

John Szoke is instructor of the Georgia Tech Supply Chain Project Management series and Lean Deployment Executive with LeanCor. John has over 15 years of hands-on supply chain experience and at LeanCor, has been responsible for leading comprehensive lean supply chain transformations for various complex organizations -- from Fortune 500 companies through small regional manufacturers. 

ATTEND AND RECEIVE A DISCOUNT CODE
As a thank you for attending, you will receive a *discount code towards our "Supply Chain Project Management: Fundamentals" course being held in Savannah, GA April 10-12, 2018 (*please note that the discount is for first-time attendees and cannot be combined with any other discount).

ABSTRACT: We study a multi-product newsvendor model with customer-driven demand substitutions, where each product, once run out of stock, can be proportionally substituted by the others. This model has been widely studied in the literature, however, due to its nonconvexity and intractability, only very limited analytical properties have been discovered nor efficient solution approaches. This paper first completely characterizes the optimal order policy when the demand is known and recasts the model as a discrete submodular maximization problem. When the demand is stochastic, we first reformulate this nonconvex model as a two-stage stochastic integer program, where the recourse decision variables are mixed integers. We further study a tight upper bound via nonanticipativity dual, which is proven to be close to the true optimal and can be computed as a linear program. Finally, numerical studies demonstrate the effectiveness of the algorithms.

BIO:

Dr. Weijun Xie graduated from Georgia Tech in 2017 and is Assistant Professor in the Department of Industrial and Systems Engineering at Virginia Tech.

For 2018, MODEX will held in conjunction with the Georgia Logistics Summit. Be sure to stop by the Georgia Tech Supply Chain & Logistics booth (#C1249) in the MODEX exhibitors area.

ABOUT MODEX

There's a reason we call MODEX "the Greatest Supply Chain Show on Earth." It's where 850 of the leading manufacturing and supply chain providers gather to showcase their latest and greatest solutions. If your business needs to maximize operational efficiency and cut costs, your solutions will be waiting at MODEX.

The industry's brightest minds will gather to network and share their perspectives on the ever-evolving state of the supply chain – where it's headed, new ideas, innovation and more. With four powerful keynotes, a supply chain education summit and more than 100 hands-on education sessions, you’re sure to find your edge.

MODEX is FREE to attend. Register today!

ABOUT THE GEORGIA LOGISTICS SUMMIT

The Georgia Logistics Summit is a unique event packed with valuable business information and networking opportunities like no other. Keynote speakers will address topics that are relevant to the operation and logistics success of your business.

Now in its 10th year, this unparalleled logistics event brings together nearly 2,000 logistics professionals from Georgia and beyond to learn the best practices and connect with colleagues and top decision-makers in our industry; making it one of the largest logistics events in the country.

You must register to attend the Georgia Logistics Summit! Visit the GLS2018 website for more information.

Please join us for...

Presented by Jennifer Pazour
Assistant Professor of Industrial and Systems Engineering
Rensselaer Polytechnic Institute (RPI) in Troy, NY

OVERVIEW

Modern distribution systems need to fulfill a wide variety of requests quickly with little warning in small units to many dispersed locations at low costs. This is fundamentally different than yesterday’s demand, which aggregated at fixed (store) locations. Existing distribution solutions, which are often static and have long decision lead times, are too rigid for today’s customers. Resulting in today’s supply chains being optimized for yesterday’s customers.  To close the gap between current supply chain operations and customer expectations, this research rethinks supply chain design.  By accessing resources on-demand, rather than through ownership, on-demand distribution platforms enable elastic supply capacity that can be scaled up and down, as well as moved in response to changing requirements.  Yet, capacity cannot be set. Instead it must be enticed from suppliers (who provide access to their resources).  Current centralized approaches to platform design excel at meeting demand commitments, but limit supplier autonomy. Decentralized approaches provide supplier autonomy, but sacrifice systematic performance and are time consuming. This research proposes a new hierarchical approach, recasting the platform's role as one providing personalized recommendations (i.e., a menu of multiple requests) to suppliers. Supplier choice can increase participation (capacity) and resource utilization when request fulfillment is combined with suppliers' original planned tasks. Prioritizing a quick time to match and efficient systematic resource coordination, the platform first decides how multiple, simultaneous recommendations are made. Then, suppliers have autonomy to select requests (if any) from the personalized recommendations. To guide design questions, we create a bilevel optimization framework. These models are novel as they capture the interdependent outcomes of supplier selections.  By harnessing the problem’s structure, we transform the computationally expensive mixed integer linear bilevel problem into a single level problem by proposing logical expressions.  For a platform only partially able to estimate suppliers' utilities, we investigate how personalized recommendation sets can be used as a coordination mechanism able to balance desirable platform, suppliers, and demand request outcomes. As the number of choices increases, suppliers have a higher chance to be recommended a request they are willing to select. This benefits the platform, up to a point. However, due to misalignment between the suppliers and the platform’s utilities, a larger number of choices lead to suppliers selecting a request with lower platform benefit. Also, as the number of choices increases, less systematic coordination occurs, and the chance for rejected requests increases. We quantify the impact of our hierarchical approach compared to a centralized, decentralized, and stable-matching approaches for a variety of scenarios and provide insights in what influences the optimal number of choices based on a ride-sharing application.

ABOUT THE SPEAKER

Jennifer Pazour, Ph.D. is an assistant professor of Industrial and Systems Engineering at Rensselaer Polytechnic Institute (RPI) in Troy, NY.  Her research and teaching focus on the development and use of mathematical models to guide decision making for logistics and supply chain challenges.  She is a recipient of a National Science Foundation Faculty Early Career Development (CAREER) Award (2018), a Johnson & Johnson Women in STEM2D Scholars Award (2018), a Young Investigator Award from the Office of Naval Research (2013), a National Academies of Science Gulf Research Program Early-Career Research Fellowship (2016), and the 2017 recipient of the Dr. Hamed K. Eldin Outstanding Early Career IE in Academia Award from IISE. She holds three degrees in Industrial Engineering (a B.S. from South Dakota School of Mines and Technology, and a M.S. and Ph.D. from the University of Arkansas), and was previously on the faculty at the University of Central Florida.  More information can be found at her research and teaching blog: http://jenpazour.wordpress.com/

Register Online by March 30th.

Please join us for...

Presented by Eric Ballot
Professor and Physical Internet Chair
Département Economie, Management | Société Mines Paristech

OVERVIEW

The Physical Internet (PI) opens many more options to deliver goods to the end destination, thanks to interconnected services. To exploit such opportunities, routing algorithms must be developed at different levels to make the PI work. In this lecture, the levels will be defined and results obtained will be compared to traditional dedicated solutions. Two main point of views will be distinguished: the shipper (how to specify routing request) and the services provider (how to publish services and make agreements). The main PI principles will be presented as well as their impacts on algorithms, data requirements and performance. Initial results show the potential, but also uncover many research opportunities for services providers to develop new services and shippers to operate their supply chain with the PI.

ABOUT THE SPEAKER

Eric Ballot is full professor of supply chain management and industrial engineering at Mines ParisTech and the director of the Management Science lab. He is the head professor of the industrial management and supply chain program. He was a visiting scholar at the MIT Sloan School of management in 2007 and a visiting professor at HongKong University since 2015. He serves a board member of several institutions and is an accredited expert by Appeal Court of Paris since 2002. Eric Ballot has been leading many research projects both with the industry Airbus, Renault, Danone… and with international academic partners such as Cambridge, GeorgiaTech… He is also the author of many research papers and books chapters. His research has been awarded and recognized at several occasions in conferences and research prizes (IESM, ILS, PREDIT). He is also leading the Physical Internet Initiative in Europe with several projects funded in France and at the European level.  

Register Online by March 30th.

Gauri Joshi, CMU

ABSTRACT:

Distributed Stochastic Gradient Descent (SGD) when running in a synchronous manner, suffers from delays in waiting for the slowest learners (stragglers). Asynchronous methods can alleviate stragglers, but cause gradient staleness that can adversely affect convergence. In this work, we present the first theoretical characterization of the speed-up offered by asynchronous methods by analyzing the trade-off between the error in the trained model and the actual training runtime (wallclock time). The novelty in our work is that our runtime analysis considers random straggler delays, which helps us design and compare distributed SGD algorithms that strike a balance between stragglers and staleness. We also present a new convergence analysis of asynchronous SGD variants without bounded or exponential delay assumptions, and a novel learning rate schedule to compensate for gradient staleness.

BIO: Gauri Joshi is an assistant professor in the ECE department at Carnegie Mellon University since September 2017. Prior to that, she worked as a Research Staff Member at IBM T. J. Watson Research Center. Gauri completed her Ph.D. from MIT EECS in June 2016. She also received her B.Tech and M. Tech in Electrical Engineering from the Indian Institute of Technology (IIT) Bombay in 2010. Her awards and honors include the Best Thesis Prize in Computer science at MIT (2012), Institute Gold Medal of IIT Bombay (2010), Claude Shannon Research Assistantship (2015-16), and the Schlumberger Faculty for the Future fellowship (2011-2015).

Supply Chain Management projects can span a wide range of project types including supply chain strategy, network analysis, facility design build, supply chain technology selection and implementation, and continuous process improvement initiatives. This course provides an overview of project management methodologies as applied in the supply chain environment. Class discussion and projects provide an understanding of how fundamental project management approaches and industry best practices can be used to effectively manage the complexities. Supply chain projects typically require managing resources, stakeholder alignment, risk management, customer impact, and effective communication across many internal and external business partners.

HOW YOU WILL BENEFIT

Upon completion of this course, you will be able to:

• Identify the business processes and functional organizations impacted by specific types of supply chain projects
• Understand the leading project methodologies (ITIL, CSM, etc.) and why complex supply chain projects require elements from each to be successful
• Understand the components of Project Management Book of Knowledge and how they can be applied in the supply chain environment
• Define project requirements and expectations for supply chain projects
• Develop the Work Breakdown Structure (WBS) for supply chain projects
• Identify the critical path for a project from its network diagram
• Be able to form and deploy a supply chain project team
• Understand how to assess, manage, and mitigate project risk
• Properly monitor, control, and close the project

WHAT IS COVERED

• Overview of Leading Project Management Methodologies & Certifications
• Types of Supply Chain Projects – Process/Functional Impacts
• PMBOK knowledge areas in context of Supply Chain Projects
• Overview of PMBOK knowledge areas in context of Supply Chain Projects
• Overview of Business Process Analysis and the SCOR model
• Defining the Project & the Effective Supply Chain Project Manager
• Why Supply Chain Projects Fail
• Defining Supply Chain Project Requirements, Teams, Roles & Responsibilities
• Traditional Phases of a Supply Chain Project
• Developing the Work Breakdown Structure for SC Projects
• Review of the Supply Chain Work Breakdown Structure
• The use of Software to Support Project Management
• Estimating Cost, Duration, and Resources
• Resource Management & Developing Dependency Diagrams
• Defining, Assessing, & Mitigating Risk
• Monitoring & Controlling the Project Plan
• Closing the Project

ON-CAMPUS COURSE MATERIALS

• Notebook of slides, notes, exercises and project management tools and templates

COURSE PREREQUISITES

None.

CERTIFICATE INFORMATION

For those interested in earning the Supply Chain Project Management Certificate, this course is the first of the four-course certificate program. To earn the certificate, participants must register and complete the following courses in the below sequence within four years, plus one elective.

1. Supply Chain Project Management: Fundamentals
2. Supply Chain Project Management: Vendor Selection & Management
3. Supply Chain Project Management: Effectively Managing Transformation Projects

For a list of courses that can be used as electives towards this certificate, please visit the Georgia Tech Professional Education website.

Supply Chain Management projects can span a wide range of project types including supply chain strategy, network analysis, facility design build, supply chain technology selection and implementation, and continuous process improvement initiatives. This course provides an overview of project management methodologies as applied in the supply chain environment. Class discussion and projects provide an understanding of how fundamental project management approaches and industry best practices can be used to effectively manage the complexities. Supply chain projects typically require managing resources, stakeholder alignment, risk management, customer impact, and effective communication across many internal and external business partners.

HOW YOU WILL BENEFIT

Upon completion of this course, you will be able to:

• Identify the business processes and functional organizations impacted by specific types of supply chain projects
• Understand the leading project methodologies (ITIL, CSM, etc.) and why complex supply chain projects require elements from each to be successful
• Understand the components of Project Management Book of Knowledge and how they can be applied in the supply chain environment
• Define project requirements and expectations for supply chain projects
• Develop the Work Breakdown Structure (WBS) for supply chain projects
• Identify the critical path for a project from its network diagram
• Be able to form and deploy a supply chain project team
• Understand how to assess, manage, and mitigate project risk
• Properly monitor, control, and close the project

WHAT IS COVERED

• Overview of Leading Project Management Methodologies & Certifications
• Types of Supply Chain Projects – Process/Functional Impacts
• PMBOK knowledge areas in context of Supply Chain Projects
• Overview of PMBOK knowledge areas in context of Supply Chain Projects
• Overview of Business Process Analysis and the SCOR model
• Defining the Project & the Effective Supply Chain Project Manager
• Why Supply Chain Projects Fail
• Defining Supply Chain Project Requirements, Teams, Roles & Responsibilities
• Traditional Phases of a Supply Chain Project
• Developing the Work Breakdown Structure for SC Projects
• Review of the Supply Chain Work Breakdown Structure
• The use of Software to Support Project Management
• Estimating Cost, Duration, and Resources
• Resource Management & Developing Dependency Diagrams
• Defining, Assessing, & Mitigating Risk
• Monitoring & Controlling the Project Plan
• Closing the Project

ON-CAMPUS COURSE MATERIALS

• Notebook of slides, notes, exercises and project management tools and templates

COURSE PREREQUISITES

None.

CERTIFICATE INFORMATION

For those interested in earning the Supply Chain Project Management Certificate, this course is the first of the four-course certificate program. To earn the certificate, participants must register and complete the following courses in the below sequence within four years, plus one elective.

1. Supply Chain Project Management: Fundamentals
2. Supply Chain Project Management: Vendor Selection & Management
3. Supply Chain Project Management: Effectively Managing Transformation Projects

For a list of courses that can be used as electives towards this certificate, please visit the Georgia Tech Professional Education website.

Supply Chain Management projects can span a wide range of project types including supply chain strategy, network analysis, facility design build, supply chain technology selection and implementation, and continuous process improvement initiatives. This course provides an overview of project management methodologies as applied in the supply chain environment. Class discussion and projects provide an understanding of how fundamental project management approaches and industry best practices can be used to effectively manage the complexities. Supply chain projects typically require managing resources, stakeholder alignment, risk management, customer impact, and effective communication across many internal and external business partners.

HOW YOU WILL BENEFIT

Upon completion of this course, you will be able to:

• Identify the business processes and functional organizations impacted by specific types of supply chain projects
• Understand the leading project methodologies (ITIL, CSM, etc.) and why complex supply chain projects require elements from each to be successful
• Understand the components of Project Management Book of Knowledge and how they can be applied in the supply chain environment
• Define project requirements and expectations for supply chain projects
• Develop the Work Breakdown Structure (WBS) for supply chain projects
• Identify the critical path for a project from its network diagram
• Be able to form and deploy a supply chain project team
• Understand how to assess, manage, and mitigate project risk
• Properly monitor, control, and close the project

WHAT IS COVERED

• Overview of Leading Project Management Methodologies & Certifications
• Types of Supply Chain Projects – Process/Functional Impacts
• PMBOK knowledge areas in context of Supply Chain Projects
• Overview of PMBOK knowledge areas in context of Supply Chain Projects
• Overview of Business Process Analysis and the SCOR model
• Defining the Project & the Effective Supply Chain Project Manager
• Why Supply Chain Projects Fail
• Defining Supply Chain Project Requirements, Teams, Roles & Responsibilities
• Traditional Phases of a Supply Chain Project
• Developing the Work Breakdown Structure for SC Projects
• Review of the Supply Chain Work Breakdown Structure
• The use of Software to Support Project Management
• Estimating Cost, Duration, and Resources
• Resource Management & Developing Dependency Diagrams
• Defining, Assessing, & Mitigating Risk
• Monitoring & Controlling the Project Plan
• Closing the Project

ON-CAMPUS COURSE MATERIALS

• Notebook of slides, notes, exercises and project management tools and templates

COURSE PREREQUISITES

None.

CERTIFICATE INFORMATION

For those interested in earning the Supply Chain Project Management Certificate, this course is the first of the four-course certificate program. To earn the certificate, participants must register and complete the following courses in the below sequence within four years, plus one elective.

1. Supply Chain Project Management: Fundamentals
2. Supply Chain Project Management: Vendor Selection & Management
3. Supply Chain Project Management: Effectively Managing Transformation Projects

For a list of courses that can be used as electives towards this certificate, please visit the Georgia Tech Professional Education website.

Essential Principles of Negotiations level-sets the participants' understanding of negotiation influence and strengthens preparation, planning and execution activities involved with both simple and complex negotiations. The program includes industry techniques and tools for traditional supplier negotiations, as well as tips for internal cross-functional leadership. Participants walk away with a standard industry and customized individual experience which includes their personal Negotiation Style “DNA” to help them embrace their own natural tendencies and strengths. The program includes mock negotiations to reinforce techniques and tactics immediately in a “no judgement zone” environment.

Who Should Attend

This course is ideal for sourcing initiative leaders, project leaders, business unit leaders, operations managers, sales leaders and procurement & supply management-related professionals who are involved with supplier selection, contract development and supplier performance management.

How You Will Benefit

• Increase emphasis negotiation conditioning and philosophy setting before and throughout the entire sourcing engagement process
• Enhance your toolbox of industry standard negotiation prep tools like the SWOT and BATNA
• Better prepare for negotiations by leveraging knowledge of key negotiation terms and counter-offer tactics
• Improve negotiation table techniques and soft skills to direct and redirect negotiation momentum
• Heighten ability to successfully utilize your traditional "comfort zone" approach in combination with your negotiation team’s strengths by leveraging Personal Negotiation Styles

What Is Covered

• Negotiation Conditioning Overview
• Negotiation Preparation Tools
• Negotiation Execution Techniques
• Stakeholder Engagement & Team Leadership
• Live Negotiations Simulation & Feedback

Bid Package Development and Supplier Selection is designed to deepen participants' knowledge base of core activities in the procurement & supply management function. The program covers the sourcing process, specifications gathering, common bid package alternatives, cross-functional collaboration and supplier evaluation & selection. Participants will walk away ready to develop bid packages more thoroughly to help drive sourcing decisions for their organizations. This "hands on" delivery focuses on the professional serving as the main liaison between the buying organization and the selling organization in the company sourcing process.

Who Should Attend

This course is ideal for sourcing initiative leaders, procurement professionals, project managers, finance analyst, contract managers and all procurement & supply management-related professionals involved with bid package development, bid package analysis, negotiations preparation, contracting and supplier selection activity.

How You Will Benefit

• Increase knowledge base of solicitation options (i.e. RFI, RFP, RFQ) and which solicitation approach to apply based on your organization's needs
• Enhance understanding of sourcing process and critical steps in the Bid Package development and delivery activities
• Better leverage and utilization of solicitation tools to drive successful development of bid packages
• Improve set up and execution of supplier selection scorecards to aid in identifying best Total Cost of Ownership alternatives
• Heighten understanding of executive communication to leverage leadership support throughout the organization

What Is Covered

• Bid Package Development Overview
• Sourcing Initiative Process Roadmap 
• Marketplace Analysis Tools
• Bid Package Alternatives & Design
• Supplier Selection & Communication

Contracting Excellence provides participants with a holistic and integrated understanding of contract law, contract types, key industry standard contract terms, and contract structure to improve their confidence when creating or modifying contract documents. The program is geared to reinforce standards of excellence for professionals who are responsible for delivering contractual agreements and mitigating financial risk for their organization.

Who Should Attend

This course is ideal for contract managers, procurement professionals, sourcing initiative leaders, project managers and all procurement & supply management-related professionals involved with bid contract development, contract execution or supplier performance management.

How You Will Benefit

• Increase knowledge base in law of agency concepts to gain more confidence with contracting.
• Enhance understanding of industry standard contract types and contract terms for more robust contract agreements.
• Better leverage sourcing category knowledge to modify existing contract elements for more holistic contract agreements. 
• Improve internal contract execution communication for better results.
• Heighten sense of executive financial impact and risk needs to gain leadership early support.

What Is Covered

• Law of Agency Overview
• Contract Agreement Types
• Defining Key Terms
• Contract Structure & Drafting
• Risk Mitigation & Communication

ABSTRACT:

We consider a problem motivated by file retrieval in cloud computing systems where storage coding is used. In such problems, each file-retrieval job consists of multiple tasks (each corresponding to the retrieval of a coded chunk of a file), and the job is completed only when all of its tasks are completed. The goal is to compute the tail probability of the job completion time. However, this is a difficult problem whereas the problem of computing tail probabilities of task completion times is relatively easy. We will show that, by assuming that the task completion times are independent, one can compute an upper bound on the tail probability of the job completion time. The result is obtained by proving that the task completions times at the various servers in the cloud system are associated. The key step in the proof can be easily understood by considering a corresponding balls-and-bins problem as we will illustrate in the talk. Joint work with Weina Wang, Mor Harchol-Balter, Haotian Jiang, and Alan Scheller-Wolf.

BIO: R. Srikant is the Fredric G. and Elizabeth H. Nearing Endowed Professor of Electrical and Computer Engineering and the Coordinated Science Lab at the University of Illinois at Urbana-Champaign. His research interests are in the areas of communication networks, cloud computing, applied probability, and machine learning. He received the 2015 IEEE INFOCOM Achievement Award, and has also received several Best Paper awards, including the 2015 INFOCOM Best Paper Award and the 2017 Applied Probability Society Best Publication Award. He was the Editor-in-Chief of the IEEE/ACM Transactions on Networking from 2013-2017.

ABSTRACT:

We discuss prescribing optimal decisions in a framework where their cost depends on uncertain problem parameters that need to be learned from historical data. We claim that robust optimization methods have an enormous untapped potential when decisions based on limited data. We indeed identify a class of robust optimization formulations which possess statistical optimality guarantees among all functions mapping data to decisions.

BIO : Bart Van Parys is currently a postdoctoral researcher working with Prof. Dimitris Bertsimas at the MIT Sloan School of Management. His research interests are situated on the interface between optimization and machine learning. In 2015 he obtained his Ph.D. in control theory at the Swiss Federal Institute of Technology (ETH) in Zurich under the supervision of Prof. Manfred Morari. He received his M.E. from the University of Leuven in 2011.

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester). Plus, enjoy a free pizza lunch!

EVENT DETAILS

Where: ISyE Main Bldg, 2nd Floor Atrium

When: Tuesday, March 13 | 11am - 2pm

What: The session will include:

• Networking opportunities in the ISyE atrium
• Food and refreshments

Please plan on staying for the duration of the event and bring copies of your resume and business cards. Dress is business casual.

REGISTER ONLINE by March 6th and upload your resume to be forwarded to the organization representatives!

EVENT SPONSOR

The event is sponsored through the generosity and support of JP Morgan Chase & Co. and APICS - Atlanta Chapter. APICS is a non-profit educational organization addressing operations management and supply chain management issues, and providing professional development opportunities to its members. APICS Membership is free for full time students. Join today at www.apics.org/join and start networking at local APIC Atlanta events. Also make sure to stop by the APICS table at the event.

ABSTRACT:

The problem of optimizing over the cone of nonnegative polynomials is a fundamental problem in computational mathematics, with applications to polynomial optimization, control, machine learning, game theory, and combinatorics, among others. A number of breakthrough papers in the early 2000s showed that this problem, long thought to be intractable, could be solved by using sum of squares programming. This technique however has proved to be expensive for large-scale problems, as it involves solving large semidefinite programs (SDPs).

In the first part of this talk, we present two methods for approximately solving large-scale sum of squares programs that dispense altogether with semidefinite programming and only involve solving a sequence of linear or second order cone programs generated in an adaptive fashion. In the second part of the talk, we focus on the problem of finding tight lower bounds on polynomial optimization problems (POPs), a fundamental task in this area that is most commonly handled through the use of SDP-based sum of squares hierarchies (e.g., due to Lasserre and Parrilo). In contrast to previous approaches, we provide the first theoretical framework for efficiently constructing converging hierarchies of lower bounds on POPs whose computation does not require any optimization, but simply the ability to multiply certain fixed polynomials together and check nonnegativity of the coefficients of the product.

BIO: Georgina Hall is a final-year PhD student and a Gordon Y. S. Wu fellow in the department of Operations Research and Financial Engineering at Princeton University. Her advisor is Prof. Amir Ali Ahmadi. She received her Bachelor of Science from Ecole Centrale Paris, France, in 2011 and her Master of Science from the same university in 2013.

ABSTRACT:

Affine policies are widely used as a solution approach in dynamic optimization where computing an optimal adjustable solution is usually intractable. While the worst case

performance of affine policies can be significantly bad, the empirical performance is observed to be near-optimal for a large class of problem instances. For instance, in the two-stage dynamic robust optimization problem with linear covering constraints and uncertain right hand side, the worst-case approximation bound for affine policies is O(√m) that is also tight (see Bertsimas and Goyal [8]), whereas observed empirical performance is near-optimal. This work aims to address this stark-contrast between the worst-case and the empirical performance of affine policies. 

We show that affine policies are provably a good approximation for the two-stage adjustable robust optimization problem with high probability on random instances

where the constraint coefficients are generated i.i.d. from a large class of distributions; thereby, providing a theoretical justification of the observed empirical performance. We also consider the performance of affine policies for an important class of uncertainty sets, namely the budget of uncertainty and intersection of budget of uncertainty sets. We show that surprisingly affine policies provide nearly the best possible approximation for this class of uncertainty sets that matches the hardness of approximation; further confirming the power of affine policies.

This talk is based is joint work with my student Omar El Housni.

BIO: Vineet Goyal is Associate Professor in the Industrial Engineering and Operations Research Department at Columbia University where he joined in 2010. He received his Bachelor's degree in Computer Science from Indian Institute of Technology, Delhi in 2003 and his Ph.D. in Algorithms, Combinatorics and Optimization (ACO) from Carnegie Mellon University in 2008. Before coming to Columbia, he spent two years as a Postdoctoral Associate at the Operations Research Center at MIT. He is interested in the design of efficient and robust data-driven algorithms for large scale dynamic optimization problems with applications in  revenue management and smart grid problems. His research has been continually supported by grants from NSF and industry including NSF CAREER Award in 2014 and faculty research awards from Google, IBM and Adobe.

ABSTRACT:

First-order methods such as ADMM and Douglas-Rachford splitting are known for their easy implementations and low per-iteration costs. What is less known is their usefulness for developing parallel algorithms for solving certain conic programs including both well-behaved and pathological (infeasible or feasible-but-unbounded) instances. In the first part of this talk, we present an algorithm and its software implementation under development for solving LPs and SOCPs using parallel and distributed computing.  In the second part, we present a method for classifying infeasible, unbounded, and other pathological conic programs based on a divergent sequence of Douglas-Rachford splitting. Our method can identify some of the weakly infeasible and weakly unbounded cases that existing interior-point solvers cannot do reliably.

BIO:  Wotao Yin
Affiliation: University of California; Los Angeles

ABSTRACT:

Existing models for disaster preparedness and response address network design and resource allocation challenges. However, these models typically adopt a global optimization point of view, which may not be attainable since they do not consider the actual decision-making process after a disaster occurs. In this talk, I will present a new mathematical model to optimally determine the design of a relief supply chain. The model is based on the realization that at times of crises, the instructions given by the authorities to the affected population regarding which facility they should visit, are often not followed, which may cause some facilities to be congested, while others to be under-utilized. The model incorporates practical considerations such as the population behavior and equitable supply allocation. An efficient optimal solution method and a heuristic algorithm based on the Tabu-search method were developed and were tested on real data obtained from the Geophysical Institute of Israel. The results indicate that the suggested modeling approach improves the entire supply chain performance.
To further improve the system performance, an incentive mechanism is added to the model, whose objective is to increase compliance with the centralized planning. A comprehensive numerical experiment and sensitivity analysis were conducted, which reveal an interesting insight, namely: even a low investment in a small proportion of the population can lead to a relatively large improvement. In addition, it is shown that proper planning of the incentive mechanism may affect the relief supply chain design decisions.
In the second part of my talk* I will introduce the Sample Referral Design (SDR) problem. The goal is to design a supply chain network for delivering test samples that diagnose and monitor the HIV epidemic, in an environment where test resources are scarce and costly. The problem is based on a case study from Tanzania, in which the objective is to minimize the expected number of infants who die due to delays in return of results. A prediction model for Early Infant Diagnosis (EID) results is developed, as well as a simulation model that describes the system. The objective is addressed through restructuring the sample referral network using analytical tools from queuing theory. The results indicate that our model can lead to significant improvements and has a great potential to save lives of many infants.
The work was performed under the supervision of Prof. Michal Tzur.
* This part was performed jointly with Dr. Dan Yamin

BIO: Reut Noham is a PhD student in the Department of Industrial Engineering, Faculty of Engineering at Tel-Aviv University. She holds a BSc. and MSc. in Industrial Engineering, both from Tel-Aviv University. Reut’s research is in the field of Humanitarian logistics, Supply chain management and Network design in healthcare systems. Her research is based on data-driven applications alongside with the theoretical analysis and mathematical modeling. Her work was funded by the Ministry of Science, Technology and Space in Israel.

ABSTRACT:

The proliferation of “big data" created by mobile devices, social media, sensor networks, satellites etc. offers the potential to make more informed decisions, but it also poses a significant challenge to the scalability of the current optimization methods and decision support tools. This raises a number of fundamental questions: How should uncertain parameters be represented so that they lend themselves to integration into optimization problems? Which observations and features of a high-dimensional parameter set are most relevant for a particular optimization problem? How can these relevant observations and features be identified efficiently? How can one maintain the computational tractability of the emerging optimization problems? In the first part of the talk I will introduce tractable approaches for reducing large datasets to smaller ones at minimal loss of information. In the second part I will describe tractable methods for recognizing balanced clusters of similar datapoints in large unstructured datasets. Both methods come with rigorous performance guarantees. Implications for practical decision-making will be highlighted.

BIO: Daniel Kuhn holds the Chair of Risk Analytics and Optimization at EPFL. Before joining EPFL, he was a faculty member at Imperial College London (2007-2013) and a postdoctoral researcher at Stanford University (2005-2006). He received a PhD in Economics from the University of St. Gallen in 2004 and an MSc in Theoretical Physics from ETH Zurich in 1999. His research interests revolve around robust optimization and stochastic programming.

ABSTRACT: Submodular maximization captures both classical problems in combinatorial optimization and practical applications that arise in other disciplines, e.g., machine learning and data mining. The size of the inputs in these applications is usually very large. Hence, it is interesting to devise approximation algorithms that in addition to providing a provable guarantee are also very fast and simple to use. In this talk, I will present several such examples from recent years.

BIO:

Roy Schwartz is an assistant professor at the Computer Science Department at the Technion. His research focuses on the design and analysis of algorithms, approximation algorithms, combinatorial optimization, the geometry of metric spaces and its applications, submodular optimization, and randomized algorithms.

ABSTRACT: 

Stochastic programming is concerned with decision making under uncertainty, seeking an optimal policy with respect to a set of possible future scenarios. While the value of Stochastic Programming is obvious to many practitioners, in reality, uncertainty in decision making is oftentimes neglected. For deterministic optimisation problems, a coherent chain of modeling and solving exists. Employing standard modeling languages and solvers for stochastic programs is however difficult. First, they have (with exceptions) no native support to formulate Stochastic Programs. Secondly solving stochastic programs with standard solvers (e.g. MIP solvers) is often computationally intractable.
 
I will talk about my research that aims to make Stochastic Programming more accessible. First, I will discuss modeling deterministic and stochastic programs in the Constraint Programming language MiniZinc - a modeling paradigm that retains the structure of a problem much more strongly than MIP formulations. Second, I will talk about decomposition algorithms I have been working on to solve combinatorial Stochastic Programs. 

BIO: David is a PhD student at Monash University in Melbourne. Before joining Monash, he completed a Bachelors degree in Systems Engineering in Switzerland, studied Robotics at University of Bristol and worked in various engineering roles. As part of his Phd, David is working on a solver system for stochastic combinatorial optimisation problems that are modeled in in the Constraint Programming language MiniZinc. In future, he would like to combine his passion for automation with the research he is doing in operations research.

Bowen Hua and Ross Baldick, University of Texas at Austin

ABSTRACT:

Unit commitment and dispatch of generation in electricity markets involves the ISO sending target quantity instructions to each generator.  Ideally, energy prices provide incentives for profit maximizing market participants to comply with efficient commitment and dispatch instructions in the short-term, and to develop new generation (based on anticipation of future energy prices) at the right place and time over the long-term. Various issues prevent this ideal, including the non-convexity of the underlying unit commitment problem, so that start-up and no-load costs of generation units may not be covered by sales of energy at locational marginal prices.  To encourage generators to comply with commitment and dispatch, non-negative profit is guaranteed by the ISO, but this guarantee necessitates non-anonymous “uplift” payments. Because energy prices alone do not provide all the information about profitability in the market, incentives for new generation to enter the market are weakened.  Moreover, the energy prices do not reveal to demand the full cost of providing energy, weakening incentives for demand response.  Both issues arguably contribute to resource adequacy problems. Convex hull pricing is a uniform pricing scheme that minimizes the amount of uplift payments. The Lagrangian dual problem of the unit commitment and economic dispatch problem has been used to determine the convex hull prices. This approach is computationally expensive for market implementations. We propose a convex programming formulation for convex hull pricing that is polynomially solvable. We describe explicitly the convex hull of individual units’ feasible commitment and dispatch decisions. We show that our formulation leads to the exact convex hull prices when ramping constraints are not present, and that the exactness is preserved when ancillary services or any linear system-wide constraints (such as the transmission constraints) are considered. When ramping constraints are considered, an exponential number of valid inequalities are needed to describe the convex hulls. In this case, we use a tractable approximation. Numerical tests are conducted on several examples found in the literature.  Although this work provides for convenient implementation of convex hull pricing, significant issues remain.  For example, the incentives for generators to reveal truthful start-up and no-load information are not well understood under either the traditional uplift approach or convex hull pricing.  Moreover, under convex hull prices, profit maximizing generators have incentives to deviate from the ISO-determined dispatch instructions.

BIO: Ross Baldick is Professor and Leland Barclay Fellow in the Department of Electrical and Computer Engineering at The University of Texas at Austin.  He has undergraduate degrees from the University of Sydney, Australia, and graduate degrees from the University of California, Berkeley.  His current research involves optimization, economic theory, and statistical analysis applied to electric power systems, particularly in the context of increased renewables and transmission.  Dr. Baldick is a Fellow of the IEEE and the recipient of the 2015 IEEE PES Outstanding Power Engineering Educator Award.

The 3rd annual Georgia Innovation Summit is scheduled for Tuesday, February 20, 2018, at the GTRI Conference Center in Atlanta. The Summit will feature dynamic presentations by leaders from the corporate world, small business and entrepreneurs, innovation organizations, universities, and government on emerging trends and innovations that will affect businesses large and small throughout Georgia. The Summit is presented by the Georgia Mentor Protégé Connection in partnership with the Georgia Department of Economic Development Entrepreneurship & Small Business Team, the Georgia Centers of Innovation, and the Georgia Tech Enterprise Innovation Institute.

A FinTech Panel will be hosted by the Georgia Tech Supply Chain and Logistics Institute with Jeff Gapusan (FinTech Catalyst, Georgia Tech) as facilitator and panelists include Tim Brown (Managing Director, GTSCL), Joe Kleinwaechter (Vice President - Innovation and Design, Worldpay | Chairman, Board of Directors for Georgia Tech Human - Computer Interaction Master's Program), Paul Noble (Founding Member of LaunchHouse | Founder and CEO, AUTIT), and PJ Bain (CEO of PrimeRevenue | Director of WiSys LLC). Please come and ask your questions!

About the Event Hosts

The Georgia Mentor Protégé Connection (MPC) is a business development program administered by the Georgia Education Foundation (GEF) in partnership with the Georgia Department of Economic Development. MPC provides opportunities for Georgia small businesses to benefit from a partnership with a corporate mentor for a period of one year. Emerging firms can build relationships, explore new technologies, learn about best practices, and benefit from the wisdom of corporate executives who are committed to the long-term health and overall growth of small businesses in Georgia. The mentor corporations are companies that are headquartered in Georgia or have a significant presence in the state. These mentor firms are leaders in their respective industries and are committed to small business development and supplier diversity. They represent a broad spectrum of industry categories, including transportation, education, logistics, manufacturing, telecommunications, consulting services and information technology.

GDEcD’s Entrepreneur and Small Business Team works hard to support small businesses and provide them with the necessary resources and services to succeed. The Entrepreneur & Small Business Team helps find solutions and effective ways to build an entrepreneurial environment to support small business through mentoring, professional development, strategic planning, and resource awareness.  The team also connects with Georgia’s business and community associations so that thousands of businesses, local leaders and practitioners are aware of resources and opportunities.  The team also supports the annual Georgia Small Business Rock Stars Awards which recognize the outstanding, unique and impactful small businesses in the state of Georgia.

Exclusive to Georgia, the Centers of Innovation provide over 1,000 businesses each year the technical industry expertise, collaborative research, and partnerships to help the State’s strategic industries connect, compete and grow globally. The Centers of Innovation provide value-added services that assist companies in Georgia’s key industries to develop new products, services, and markets through partnerships with state, federal and private institutions that comprise Georgia’s Innovation Ecosystem. This Ecosystem contains a large and diverse array of participants and resources, including the state’s universities and technical colleges, which contribute to and are necessary for ongoing innovation in the state’s modern economy.

The Enterprise Innovation Institute (EI2) is Georgia Tech’s business outreach organization and serves as the primary vehicle to achieve Georgia Tech’s goal of expanded local, regional, and global outreach. EI2 is the nation’s largest and most comprehensive university-based program of business and industry assistance, technology commercialization, and economic development. EI2 serves startups, industry, the public sector, and students to help increase companies bottom line, improve competitiveness, and positively impact the economy. In addition, EI2 provides connections to Georgia Tech’s vast resources, including world-class research, state-of-the-art facilities, internationally recognized experts and upper-echelon students.

ONLINE REGISTRATION

Home Delivery World 2018 is returning to Atlanta for its sixth year as THE forum where retailers, grocers and solution providers gather to discuss delivery operations challenges and trends.

Since its inception, Home Delivery World has served some of the biggest retailers and most forward-thinking grocers. There has never been a bigger opportunity to meet and collaborate with your peers under the same roof.

Georgia Tech Students!

SCL is again serving as an official Education and Research Partner with our own Tim Brown moderating roundtables on "Innovation Case Studies" on the first day of the event and "Vertical Supply Chain & Uberization" on the second. The event is open to the public and "Exhibition-only" passes are free.

A limited number of "All Access" passes will be made available to students in exchange for a day of blogging (grants access to activities in addition to the expo…stop by the SCL suite - Groseclose 228 or email event@scl.gatech.edu for more information).

Some key topics at the conference will be: 

• Understanding the four principles: customer obsession rather than competitor focus; passion for invention; commitment to operational excellence and long-term thinking
• Exploring new innovations within and beyond the last mile through new brands, small businesses, and the Northeast’s busiest people
• Expanding outside the U.S using creative tech enabled software
• Cleverly placing fulfilment centers to achieve lower picking costs, higher picking rates and less substitutions
• Analyzing a real-world example of how a digital core powers a highly dynamic and agile supply chain to make real-time purchase, routing, and pricing decisions
• Provide quantitative and qualitative attributes for the products leveraging integrated Blockchain, IoT, and Cognitive technologies
• The best practices that can help you achieve logistical excellence across your delivery ecosystem
• Evaluating the strategies, processes and technologies that you can implement to optimize your workforce and adapt to consumer behaviors

Learn More | Register to Attend

• If you are interested in home delivery or discussing topics like reverse logistics, direct fulfillment, omnichannel fulfillment, multichannel returns, real-time visibility for inventory, and international expansion you won't want to miss this event. Visit the event website to view the agenda, download the event brochure 

About Attending

With the FREE "Expo" Pass, you will have the opportunity to meet, learn, and network anywhere on the expo floor. Leading retailers, etailers, and grocers from across the globe will be there!

 The Master of Science in Analytics is an interdisciplinary degree program that leverages the strengths of Georgia Tech in statistics, operations research, computing, and business by combining the world-class expertise of the Scheller College of Business, the College of Computing, and the College of Engineering.  We would like to invite you to participate in our 2018 Spring MS Analytics Career Fair, which is a unique opportunity to meet our students and recruit top analytics talent for internships and full-time positions at your company.

Tuesday, February 27^th, 2018

5:00 – 6:00PM Check-In & Set-up; 6:00 – 8:00PM Career Fair

Georgia Institute of Technology

Klaus Advanced Computing Building Atrium

266 Ferst Drive, NW

Atlanta, GA  30332

 Registration Deadline:  Friday, February 19^th, 2018

For more info and to register à

Questions?  Contact Yvonne Smith at ysmith@isye.gatech.edu or 404-385-1655

Author: Ji Chen and Xiaodong Li

ABSTRACT: 

In this talk, we study nonconvex matrix completion from a perspective of assumption-free approximation: with no assumptions on the underlying positive-semidefinite matrix in terms of rank, eigenvalues or eigenvectors, we established the low-rank approximation error based on any local minimum of the proposed objective function. The approximation error consists of an over-parametrization term and an under-parametrization term, both of which are also illustrated by numerical experiments. As interesting byproducts, when certain assumptions are imposed on the underlying matrix, our results improve the state-of-the-art sampling rate results in the literature of nonconvex matrix completion with no spurious local minima. We also discussed how the proposed low-rank approximation framework is applied to memory-efficient Kernel PCA, and numerical experiments also show that our approach is competitive in terms of approximation accuracy compared to the well-known Nystrom algorithm. 

BIO:  Dr. Xiaodong Li is an assistant professor in the statistics department at UC Davis. Prior to that, he worked in the statistics department of Wharton School at University of Pennsylvania for two years. He got his Ph.D of mathematics at Stanford University in 2013, and his BS at Peking University in 2008. He has general research interests in machine learning, statistics, optimization and signal processing. Particularly, he is interested in the connection between optimization/spectral methods and the underlying low-rank/spectral structures. His papers have been published in various journals of statistics, mathematics and engineering such as AoS, ACHA, FoCM, JACM, IEEE TIT, etc.