Smarter data-driven decision-making by integrating prediction and optimization

Abstract:

Big data provides new opportunities to tackle one of the main difficulties in decision-making systems – uncertain behavior driven by the unknown probability distribution. Instead of the classical two-step predict-then-optimize (PTO) procedure, we provide smarter data-driven solutions by integrating these two steps. In the first half of this talk, we focus on a multi-period inventory replenishment problem with uncertain demand and vendor lead time (VLT), with accessibility to a large quantity of historical data. Different from the traditional two-step predict-then-optimize (PTO) solution framework, we propose a one-step end-to-end (E2E) framework that uses deep-learning models to output the suggested replenishment amount directly from input features without any intermediate step. The E2E model is trained to capture the behavior of the optimal dynamic programming solution under historical observations, without any prior assumptions on the distributions of the demand and the VLT. This algorithm is currently implemented in production at JD.com to replenish thousands of products. In the second half of this talk, I will move to a more general setting of the contextual stochastic optimization problem. We propose an integrated conditional estimation-optimization (ICEO) framework that estimates the underlying conditional distribution using data while considering the structure of the downstream optimization problem. We show that our ICEO approach is asymptotically consistent and further provide finite performance guarantees in the form of generalization bounds. We also discuss the computational difficulties of performing the ICEO approach and propose a general methodology by approximating the potential non-differentiable oracle. We also provide a polynomial optimization solution approach in the semi-algebraic case. The concept of E2E, which uses the input information directly for the ultimate goal, shortens the decision process and can also be useful in practice for a wide range of circumstances beyond supply chain management.

Bio:

Meng Qi is a Ph.D. Candidate in the Department of Industrial Engineering and Operations Research at University of California, Berkeley, where she is advised by Prof. Zuo-Jun (Max) Shen. Previously, she graduated from Tsinghua University with a B.S. in Physics. Her research focuses on developing more automatic and robust data-driven solutions for decision-making with uncertainty, combining tools and concepts from optimization, machine learning, and statistics. From an applications perspective, her research focuses on supply chain management and retail operations. As a part of it, she actively collaborates with industrial partners in e-commerce.

Pseudo-Competitive Games and Algorithmic Pricing

Abstract:

Algorithmic pricing is increasingly a staple of e-commerce platform operations; however, while such data-driven pricing techniques are known to work well in non-strategic environments, their performance in competitive settings remains poorly understood. To this end, we investigate market outcomes that may arise when multiple competing firms deploy local price experimentation algorithms while treating their market environment as a black-box. For price-competition games induced by a broad class of well-validated customer behavior models, we demonstrate that price trajectories resulting from natural local learning dynamics may converge to outcomes in which firms can experience unbounded losses in revenue compared to the best price equilibrium. We moreover design a novel learning algorithm to address this concern. 

This work falls under a broader range of questions in people-centric operations, wherein new markets and platforms fail to fully harness advances in optimization and AI due to inadequately accounting for the utilities of agents, firms, and society as a whole. Such questions arise both in competitive settings, as discussed above, but also in collaborative settings; I will highlight this in the latter part of my talk by briefly discussing my work on the design of multi-modal transportation systems.

Bio: 

Chamsi Hssaine is a final-year Ph.D. candidate in the School of Operations Research and Information Engineering at Cornell University, where she is advised by Professor Sid Banerjee. She graduated magna cum laude from Princeton University in 2016, with a B.S. in Operations Research and Financial Engineering. Her research centers around algorithm and incentive design for smart societal systems, with a focus on incorporating more realistic models of behavior under incentives, and better understanding the effect of policy decisions on stakeholders. Chamsi was selected for the 2020 Rising Stars in EECS workshop at UC Berkeley, as well as the 2020 Rising Scholars conference at the Stanford Graduate School of Business. In 2019, she was a visitor at the Simons Institute for the program on Online and Matching-Based Market Design. Her paper "Real-Time Approximate Routing for Smart Transit Systems" (joint with Sid Banerjee, Noémie Périvier, and Samitha Samaranayake) was a finalist for the 2021 INFORMS Minority Issues Forum Paper Competition.

Stochastic optimization under distributional shifts

Abstract:

Learning problems commonly exhibit an interesting feedback
mechanism wherein the population data reacts to decision makers'
actions. This is the case for example when members of the population
respond to a deployed classifier by manipulating their features so as
to improve the likelihood of being positively labeled. In this way,
the population is manipulating the learning process by distorting the
data distribution that is accessible to the learner. In this talk, I will present some recent modelling frameworks and algorithms for dynamic problems of this type, rooted in stochastic optimization and game theory.

Joint work with Evan Faulkner (UW), Maryam Fazel (UW), Adhyyan Narang
(UW), Lillian J. Ratliff (UW), Lin Xiao (Facebook AI)

Bio:

Dmitriy Drusvyatskiy received his PhD from the Operations
Research and Information Engineering department at Cornell University
in 2013, followed by a post doctoral appointment in the Combinatorics
and Optimization department at Waterloo, 2013-2014. He joined the
Mathematics department at University of Washington as an Assistant
Professor in 2014, and was promoted to an Associate Professor in 2019.
Dmitriy's research broadly focuses on designing and analyzing
algorithms for large-scale optimization problems, primarily motivated
by applications in data science. Dmitriy has received a number of
awards, including the Air Force Office of Scientific Research (AFOSR)
Young Investigator Program (YIP) Award, NSF CAREER, INFORMS
Optimization Society Young Researcher Prize 2019, and finalist
citations for the Tucker Prize 2015 and the Young Researcher Best
Paper Prize at ICCOPT 2019. Dmitriy is currently a co-PI of the NSF
funded Transdisciplinary Research in Principles of Data Science
(TRIPODS) institute at University of Washington.

Research currently supported by NSF CAREER DMS 1651851 and NSF CCF 1740551.

E-commerce warehousing and some new results on picker routing

Abstract:

In the wake of ever-increasing e-commerce sales, warehouses have evolved to technology-enriched, mission-critical fulfillment factories. This talk reviews suitable e-commerce warehouse structures such as scattered storage and robot-assisted order picking and investigates the routing problems that are to be solved within these novel warehouses. In the very core of traditional picker-to-parts warehouses is the classical picker routing problem, which equals the traveling salesman problem (TSP) but is well-known to be efficiently solvable in the parallel-aisle structure of warehouses. New warehouses require the solution of other well-known extended routing problems, such as the clustered TSP, the generalized TSP, and the prize collecting TSP. All these routing problems are well-known to be strongly NP-hard for general graphs. This talk shows how the warehouse structure impacts this complexity status and how the parallel-aisle structure of warehouses can be exploited to improve the efficiency of routing algorithms.

Bio:

After some industry practice at IBM Global Services, Nils joined the Friedrich Schiller University in Jena (Germany), where he became a full professor for operations management. His main research interests are in the fields of facility logistics, warehousing, transportation, and automobile production. To solve industry problems in these areas, Nils applies mathematical modelling and combinatorial optimization techniques, always based on a thorough analysis of computational complexity. He has published over 150 research papers in many of the top optimization and logistics journals. Among others he is a member of the editorial boards of Transportation Science and EJOR.

ABSTRACT:

Dynamic watermarking, as an active intrusion detection technique, can potentially detect replay attacks, spoofing attacks, and deception attacks in the feedback channel for control systems. In this talk, we will discuss our recent work on a novel dynamic watermarking algorithm for finite-state finite-action Markov decision processes and present bounds on the mean time between false alarms, and the mean delay between the time an attack occurs and when it is detected. We further compute the sensitivity of the performance of the control system as a function of the watermark. We demonstrate the effectiveness of the proposed dynamic watermarking algorithm by detecting a spoofing attack in a sensor network system.

Bio: Abhishek Gupta is an assistant professor at Electrical and Computer Engineering at The Ohio State University. He completed his Ph.D. in Aerospace Engineering (2014), MS in Applied Mathematics (2012), and MS in Aerospace Engineering (2011), all from University of Illinois at Urbana-Champaign (UIUC). He completed his undergraduate in Aerospace Engineering from Indian Institute of Technology, Bombay, India (2005-09). His research develops new theory and algorithms for stochastic control problems, games, and optimization problems, with applications to secure cyberphysical systems and develop market mechanisms for deep renewable integration. He is a recipient of Kenneth Lee Herrick Memorial Award at UIUC and Lumley Research Award at OSU.

Integrating dual scheduling modes in workforce management

Abstract:

Motivated by the emergence of self-scheduling platforms for volunteers and the continued need to meet time-specific needs of nonprofit organizations, this research explores modeling approaches and scheduling policies to effectively manage workforce scheduling for organizations with dual scheduling modes.  As an illustrative example, we consider a nonprofit organization that provides relief to those impacted by disasters using both volunteers who schedule themselves and staff members who are assigned to shifts.  We explore the advantages of scheduling policies that explicitly account for the two groups, balancing the need to cover time slots to meet demand with the desire to offer meaningful and convenient opportunities to volunteers such that they maintain their engagement with the organization.  We present a case study based on operational data from our collaborators and more general insights based on synthesized data.  This is joint work with Mariana Escallon-Barrios and Reut Noham.

Bio:

Dr. Karen Smilowitz is the James N. and Margie M. Krebs Professor in Industrial Engineering and Management Science at Northwestern University, with a joint appointment in the Operations group at the Kellogg School of Business.  Dr. Smilowitz is an expert in modeling and solution approaches for logistics and transportation systems in both commercial and nonprofit applications.  Dr.  Smilowitz is the founder of the Northwestern Initiative on Humanitarian and Nonprofit Logistics.  She has been instrumental in promoting the use of operations research within the humanitarian and nonprofit sectors through the Woodrow Wilson International Center for Scholars, the American Association for the Advancement of Science, and the National Academy of Engineering, as well as various media outlets.  Dr. Smilowitz is the Editor-in-Chief of Transportation Science.  

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).
 

EVENT DETAILS

On Campus/In-Person (ISyE Main Building Atrium)

Thursday, November 11 | 11am-2pm ET

Virtual/Online (Career Fair Plus)

Friday, November 12 | 9am - 3pm ET

MORE INFORMATION AND EVENT REGISTRATION

Visit https://www.scl.gatech.edu/outreach/supplychainday for a list of attending organizations and links to register.

EVENT SPONSOR

The event is sponsored through the generosity and support of the CSCMP Atlanta Roundtable. Students, young professionals, academics and military personnel are eligible for a discounted membership. Make sure to stop by the Atlanta CSCMP table at our on campus event and visit https://www.atlantacscmp.org/join.

Please RSVP to attend, so we have enough representatives and food for the attendees.

Abstract: Despite the great empirical success of deep reinforcement learning, its theoretical foundation is less well understood. In this work, we make the first attempt to theoretically understand the deep Q-network (DQN) algorithm from both algorithmic and statistical perspectives. Specifically, we focus on a slight simplification of DQN that fully captures its key features. Under mild assumptions, we establish the algorithmic and statistical rates of convergence for the action-value functions of the iterative policy sequence obtained by DQN. In particular, the statistical error characterizes the bias and variance that arise from approximating the action-value function using a deep neural network, while the algorithmic error converges to zero at a geometric rate. As a byproduct, our analysis provides justifications for the techniques of experience replay and target network, which are crucial to the empirical success of DQN. Furthermore, as a simple extension of DQN, we propose the Minimax-DQN algorithm for zero-sum Markov game with two players. Borrowing the analysis of DQN, we also quantify the difference between the policies obtained by Minimax-DQN and the Nash equilibrium of the Markov game in terms of both the algorithmic and statistical rates of convergence.

Bio: Jianqing Fan is a statistician, financial econometrician, and data scientist. He is Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and Professor of Operations Research and Financial Engineering at the Princeton University where he chaired the department from 2012 to 2015. He is the winner of The 2000 COPSS Presidents' Award, Morningside Gold Medal for Applied Mathematics (2007), Guggenheim Fellow (2009), Pao-Lu Hsu Prize (2013) and Guy Medal in Silver (2014). He got elected to Academician from Academia Sinica in 2012.

About this event

Abstract: In large service systems, such as cloud computing systems, there are different classes of jobs and of servers such that each job class can only be done on a subset of the server classes, due to data locality and other constraints. Similarly, there are often compatibility constraints in dynamic matching systems such as platforms for car sharing and waitlists for organ transplants. Under Markovian assumptions, the steady-state distributions for such systems have been shown to have a simple “product-form” structure.  I will describe a unified framework for these models that provides a common simple proof for the product-form results at a detailed state description and provides a simple, state-aggregated, view for analyzing waiting time distributions.

Joint work with Ivo Adan, Igor Kleiner, Kristen Gardner, and Gideon Weis

Bio: Rhonda Righter is a Professor and past Chair of the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. Before joining the faculty at Berkeley she taught at the Leavey School of Business at Santa Clara University. Her PhD is in Industrial Engineering and Operations Research from UC Berkeley, her BS is in applied math and business from Carnegie Mellon. Her primary research and teaching interests are in the general area of stochastic modeling and optimization, especially as applied to service, manufacturing, telecommunications, and large-scale computing systems. She is an associate editor for Queueing Systems, Probability in the Engineering and Informational Sciences, Stochastic Models, and the INFORMS Service Science Journal. She has also served on the editorial boards of Management Science, Operations Research, Operations Research Letters, the Journal of Scheduling, and Naval Research Logistics. She is the past (founding) Chair of the Applied Probability Society (APS) of INFORMS and is currently Chair of the APS Prize Committee.

Nursing Home Staff Networks and COVID-19

Abstract:

Skilled nursing homes (SNFs) accounted for a disproportionate share of COVID-19 fatalities worldwide, with outbreaks persisting despite the March 2020 nationwide ban on visitors. Using device-level geolocation data for 50 million smartphones, we analyze SNF connections via shared staff and observe 500,000 individuals entering at least one SNF, with 5.1% entering two or more facilities. Nursing homes share connections with 7.1 other facilities, on average. Network measures of connectivity, including node degree, strength and Eigenvector centrality, are highly predictive of COVID-19 cases, whereas traditional regulatory quality metrics are unimportant in predicting outbreak size.

Bio:

Elisa Long is an Associate Professor of Decisions, Operations, and Technology Management at UCLA Anderson, and was previously on the faculty at the Yale School of Management. Her research spans topics in healthcare operations, including epidemic control, hospital resource allocation, breast cancer decision-making, and most recently, nursing home staff networks during the COVID pandemic. She teaches courses on Data & Decisions and Healthcare Analytics, and has received several teaching and research awards. She earned a PhD in Management Science & Engineering from Stanford, and a BS in Operations Research from Cornell.

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).
 

EVENT DETAILS

On Campus/In-Person (ISyE Main Building Atrium)

Thursday, September 9 | 10am-3pm ET

Virtual/Online (Career Fair Plus)

Friday, September 10 | 9am - 3pm ET

MORE INFORMATION AND EVENT REGISTRATION

Visit https://www.scl.gatech.edu/outreach/supplychainday for a list of attending organizations and links to register.

EVENT SPONSOR

The event is sponsored through the generosity and support of the Association of Supply Chain Management. Join today and start networking at local ASCM Atlanta Chapter events. Also make sure to stop by the ASCM Atlanta Chapter table at the event.

The requirement for high levels of customer service, increasing numbers of SKUs and high labor costs have dramatically increased the complexity of warehouse operations. It is no longer sufficient to manage a warehouse based on a simple, arbitrary “ABC” classification of SKUs, which treats all those in a category as if they were identical. Instead, each decision – such as where to store or where to pick product – must be based on careful engineering and economic analysis. Each SKU must identify its own cheapest, fastest path through the warehouse to the customer and then compete with all the other SKUs for the necessary resources. This results in warehouse operations that are finely tuned to patterns of customer orders and maximally efficient. Learn the concepts necessary to address modern warehouse trade-offs between space and time in optimizing and managing your warehouse.

Essential learning for those who are seeking cost reductions through better handling methods. Also valuable for those who must replace, upgrade, or add material handling equipment. The two-day course will include case examples and a guided exercise to ensure mastery of the techniques presented.

WHO SHOULD ATTEND

Supply chain and logistics consultants, supply chain engineers and analysts, facility engineers, and warehouse supervisors and team leaders

HOW YOU WILL BENEFIT

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

• Exchange space for time (or vice versa) to better meet business objectives.
• Understand when to use dedicated storage and when to use shared storage.
• Identify the most convenient locations in a warehouse based on an economic model.
• Identify patterns in customer orders and exploit these to speed fulfillment.
• Evaluate warehouse performance.
• Optimally size and stock a forward pick area.
• Understand the best practices in order-picking.

WHAT IS COVERED

• Warehouse performance
• Modern warehouse trade-offs
• Size and stocking optimization
• Order-picking best practices
• Automation

COURSE MATERIALS

• Online access to course material in electronic format 
• Access to an e-copy of the book “Warehouse & Distribution Science” as well as access to an accompanying suite of software to aid in warehouse analytics and optimization.

COURSE PREREQUISITES

None.

CERTIFICATE INFORMATION

This course is part of the Distribution Operations Analysis & Design (DOAD) Certificate.

A free, half-hour informational webinar relating to our PSM Leadership program designed to accelerate the leadership and executive presence of professionals responsible for driving cost management and category sourcing excellence. The program is driven by key competency areas (contract development, negotiation and influence, financial analysis, etc..) and embeds team leadership and stakeholder management into every competency, case study, and class challenge.

Register online at https://bit.ly/psmcert2021 to attend.

WHO SHOULD ATTEND

Procurement and supply management senior managers, directors and VPs with direct reports looking to deepen application skills and sharpen procurement leadership tools. ABOUT THE PRESENTER G’Sean Williams is a Lead Instructor for the Georgia Tech Supply Chain and Logistics Institute's Procurement & Supply Management (PSM) Leadership Certificate courses where he leverages his experience in negotiating deals, managing contracts and leading global procurement teams. He is also Executive Director of SMS-Advisors’ Supply Chain and Supplier Performance group. Prior to becoming a Partner at SMS-Advisors, G. Sean held various Procurement & Supply Management Manager and Director level positions in Corporate America for companies including Intel, WestRock, and Bristol Myers Squibb. He is currently Senior Director / Head of Indirect Procurement at Floor & Decor.

PSM COURSE SERIES

Offered throughout September 2021 virtually/online. Learn more at  https://www.scl.gatech.edu/PSM.

This course focuses on defining, executing, and improving the S&OP process. Participants will be introduced to the appropriate stakeholders of S&OP, the importance of S&OP to corporate performance, S&OP cadence, and the use of visionary technology to bring S&OP to the next level. Business cases will be used to show concrete examples of companies where S&OP is effectively applied.

WHO SHOULD ATTEND

• Chief Operating Officers, Supply Chain, Sales, Marketing and Finance Management Executives (Directors, VPs, EVPs)
• Supply Chain and Logistics Managers, Consultants, Supervisors, Planners, and Engineers
• Supply Chain Education and Human Resource Management personnel
• Inventory and Demand Planners
• Procurement and Sourcing Analysts and Managers
• Manufacturing Planners, Analysts, and Managers
• Sales Operations Managers, Analysts, Planners, Supervisors, Directors

HOW YOU WILL BENEFIT

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

• Understand the need for an S&OP cycle in a company
• Apply principles key to success of an S&OP process
• Experience true market examples relevant to their businesses

LEARNING OBJECTIVES

• Learn how to identify and apply best fit S&OP process and technology enablers to your organization and make it a reality based process.
• Walk through a complete simulated S&OP cycle supported by a technology enabler.
• Understand the interaction and integration between the financial and operation levels of S&O.
• Learn the key components of an effective S&OP business case through discussion of real life examples of how companies have benefited from the implementation of best practices in S&OP.

WHAT IS COVERED

• Defining the S&OP process before adopting technology
• The advantages of value based and reality based S&OP
• Why S&OP needs to be integrated closely with operational planning
• What is the scope of each role in the S&OP Cycle
• What are the most valuable outputs and results of the S&OP Cycle
• How can technology enable companies to take performance to the next level
• Experience a complete simulated technology-enabled S&OP Cycle

Category Management and Sourcing Leadership 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.

The online version of the course is comprised of (3) instructor-led LIVE group webinars, homework, and pre-work (e.g. installing and testing software on your computer, testing connectivity with Canvas LMS and BlueJeans meeting software, etc.) to be completed before the first day of the course.

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

Abstract: The Industrial Internet of Things enables the connection of sensors, devices, and systems, and the rapid communication of data among them. Such data provide unprecedented opportunities for smart manufacturing, including real-time monitoring and optimal decision making. This talk will reflect on the advances of manufacturing in response to several technological revolutions. An example of lithium-ion battery manufacturing will be used to discuss the characteristics of smart manufacturing and the need for data analytics research. In addition, connection among customers, manufacturers, and suppliers is also creating a new paradigm of personalized manufacturing where customers actively participate in the design and fabrication of products or product components. Recent research activities in personalized manufacturing will also be highlighted. 

Bio: S. Jack Hu joined the University of Georgia (UGA) as the UGA Foundation Distinguished Professor of Engineering and Senior Vice President for Academic Affairs and Provost on July 1, 2019. As Provost he oversees instruction, research, public service and outreach, and information technology. Prior to joining UGA, he was the Vice President for Research, the J. Reid and Polly Anderson Professor of Manufacturing, Professor of Mechanical Engineering, and Professor of Industrial and Operations Engineering at the University of Michigan.

Hu has authored or co-authored nearly 200 peer-reviewed journal articles related to his research in manufacturing systems, assembly, and engineering statistics. He holds six patents and has worked closely with several industry partners to enhance manufacturing quality and productivity.

Hu is a member of the U.S. National Academy of Engineering and a foreign member of the Chinese Academy of Engineering. He is a Fellow of the American Society of Mechanical Engineers (ASME), the Society of Manufacturing Engineers (SME), and the International Academy for Production Engineering (CIRP). He is the recipient of the ASME William T. Ennor Manufacturing Technology Award, the SME Gold Medal, and several best paper awards.

Reception immediately following the lecture.

This course is the fourth in the 4-course Supply Chain Analytics Professional certificate program. It incorporates learning advanced analytics and mathematical optimization to find solutions for supply chain problems. You’ll learn how to use linear programming, mixed integer programming, and heuristics to conduct prescriptive analytics related to production processes, distribution networks, and routing. The course serves as a capstone for the program by culminating in a hackathon where you’ll design networks, inventory policies, and scenarios and then evaluate the outcomes via simulations.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

• Use mathematical optimization to transform Supply Chain Management (SCM) processes.
• Apply LP, MIP, and heuristics to SCM, particularly in production planning, routing, and network design.
• Utilize PowerBI and Python in optimization projects.
• Participate in a hackathon that pulls together everything learned throughout the certificate program.

What Is Covered

• Role of mathematical optimization in addressing complex SCM challenges  
• Appropriate application of linear programming (LP), mixed integer programming (MIP), and heuristics
• Evaluation of production processes, distribution networks, and routes using optimization
• Ability to pull together all content of the certificate program into a prescriptive analytics project
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

Randomized Load Balancing: the queue at the cavity at work

Abstract:

Randomized load balancing algorithms in large-scale computing systems have received a lot of attention in the last few years. In this seminar we illustrate that the performance of many of these algorithms can be accessed using "the queue at the cavity approach". This approach is an approximation method that is known to yield exact results in the large-system limit in some particular cases. Apart from illustrating how this approach works on a number of algorithms, we also touch upon some open problems in this area.

Short Bio:

Benny Van Houdt is a professor at the department of Mathematics and Computer Science at the University of Antwerp (Belgium), where he also obtained his Phd in 2001. He has been a post-doctoral fellow of the FWO-Flanders from October 2001 until October 2007. He is currently the Editor-in-Chief of the Performance Evaluation journal (since Jan 2018), a senior associate editor of ACM ToMPECS (since 2014) and an editorial board member of Stochastic Models (since 2016). He has been a member of the editorial board of Operations Research Letters (2007-2017) and Performance Evaluation (2011-2017).
Benny is the (co)recipient of various awards including best paper awards at ACM Sigmetrics, IFIP Performance, ITC, QEST and Valuetools. He is an elected member and officer of the IFIP working group 7.3 on Computer System Modeling and has published papers in a variety of journals such as IEEE/ACM Trans. on Networking, IEEE Trans. on Information Theory, Communications, IEEE JSAC, IEEE/OSA JOCN, Performance Evaluation, QUESTA, Journal of Applied Probability, Adv. In Applied Probability, Operations Research Letters, INFORMS JOC, EJOR, Stochastic Models, Computer Networks, Naval Research Logistics, etc.

If you are interested in attending, please review the below information and register online.

SESSION OVERVIEW

The final session of the series will host a technical in-depth discussion around how AI and Machine Learning is transforming enterprises by optimizing planning processes.

SESSION SPEAKER

Nelson Grajales is Machine Learning Software Engineer at o9 Solutions. He has over 4 years of professional experience in Analytics/AI/Machine Learning using tools such as SAS, AWS and o9. Currently at o9 Solutions, Nelson works with customers to scale and support their Machine Learning algorithms on the o9 platform. Nelson has BS in Electrical Engineering from The University of Houston.

The session will be moderated by Alan Erera, Associate Chair for Research and Co-Executive Director, Georgia Tech Panama Logistics Innovation & Research Center and UPS Professor of Logistics

Register Online for this upcoming SCL IRC seminar

Attendance is complimentary and this session is open to the public.

Essentials of Negotiations and Stakeholder Influence 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.

The online version of the course is comprised of (4) instructor-led LIVE group webinars, homework, and pre-work (e.g. installing and testing software on your computer, testing connectivity with LMS and meeting software, etc.) to be completed before the first day of the course.

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

Contracting and Legal Oversight 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.

The online version of the course is comprised of (3) instructor-led LIVE group webinars, homework, and pre-work (e.g. installing and testing software on your computer, testing connectivity with Canvas LMS and BlueJeans meeting software, etc.) to be completed before the first day of the course.

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

Short Bio:

Fatma Kılınç-Karzan is the Frank A. and Helen E. Risch Faculty Development Chair and Associate Professor of Operations Research at Tepper School of Business, Carnegie Mellon University. She holds a courtesy appointment at the Department of Computer Science as well.  She completed her PhD at the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology in 2011. Her research interests are on foundational theory and algorithms for convex optimization and structured nonconvex optimization, and their applications in optimization under uncertainty, machine learning and business analytics. Her work was the recipient of several best paper awards, including 2015 INFORMS Optimization Society Prize for Young Researchers and 2014 INFORMS JFIG Best Paper Award. Her research has been supported by generous grants from NSF and ONR, including an NSF CAREER Award.

    Abstract: The macroscopic description of the dynamics of a variety of queueing models can formally be posed as a free boundary problem (FBP). For example, in the (single / many server) earliest-deadline-first model, the boundary of the support of the empirical deadline distribution corresponds to the shortest deadline among currently available jobs, and this boundary plays a main role in the dynamics. For the (parallel server) join-the-shortest-workload model, it is the shortest currently available workload. The relation between the queueing models and their formal macroscopic description is important to establish rigorously. However, this task is often very hard as the FBPs may be ill posed. The talk will describe progress achieved by (1) a recently developed approach to these FBPs via Skorohod maps, (2) tools from hydrodynamic limits for interacting particle systems governed by FBPs.

    Bio: Rami Atar is with the Viterbi Faculty of Electrical Engineering of the Technion, Haifa, Israel, where he holds the Lady Davis Chair is Sciences. He is a Fellow of the IMS. His research interests are in fluid, diffusion and large deviation scale analysis of queueing models, PDE techniques in control and games, model robustness via Renyi divergence, and Skorohod maps in measure space.

In this talk, I will describe a stochastic model of the process leading to order cancellations for a single item so that retailers may find fulfillment policies that effectively use this information along with shipping costs between various locations. We describe iterative algorithms based on Infinitesimal Perturbation Analysis (IPA) that converge to optimal and locally optimal policies within certain flexible policy classes for the multiple-location version of this model, and show their empirical performance on simulations based on real data from a high-end North American retailer. Time permitting, I will sketch extensions of the basic model to account for multiple stages of fulfillment and modeling pick failures at stores.

This talk is based on the dissertation of Jeremy Karp at CMU describing work carried out jointly with Prof. Sridhar Tayur (CMU) and Dr. Srinath Sridhar (Onera Inc), with recent contributions from Su Jia and Sagnik Das from CMU.

Bio:    http://www.contrib.andrew.cmu.edu/~ravi/

Dr. R. Ravi is Andris A. Zoltners Professor of Business, and Professor of
Operations Research and Computer Science at Carnegie Mellon University.

Ravi received his bachelor's degree from IIT, Madras, and Master's and doctoral
degrees from Brown University, all in Computer Science.

Ravi's research focuses on models, methods and applications of discrete
optimization, and their applications in the intersection of business and technology.
He served as area editor for "Operations Research" in charge of the discrete
optimization area between 2012-2017. He has formerly served as associate editor
in the ACM Transactions on Algorithms, Management Science, Networks and
Journal of Algorithms. He has also served on several international program
committees including as the program chair for the 2008 IEEE Foundations of
Computer Science (FOCS) conference. Ravi has co-authored two books, over 130
publications, and has a h-index of over 50.

Ravi has been at the Tepper School of Business since 1995 where he served as the
Associate Dean for Intellectual Strategy from 2005-2008. He was Chair of the
Future Educational Delivery Committee that launched the online hybrid Tepper
MBA in 2013. He was elected a Fellow of the INFORMS in 2017.

Gene regulation refers to how genes in a cell are switched on or off to determine a cell’s fate and function. It is central to an extraordinary range of biological phenomena from development to disease, as well as the evolution of diverse life forms. My group’s research develops and uses computational tools, based on probabilistic inference, machine learning, and biophysics-inspired models, to answer unsolved and topical questions related to gene regulation in diverse biological processes.

Scientists today use diverse high-throughput technologies to generate “multi-omics” (genomics, transcriptomics, metabolomics, etc.) data that provide detailed views of a biological process from different vantage points. We develop principled approaches to analyze these multi-omics data in an integrated manner and uncover regulatory mechanisms underlying the process, including key regulators and networks of regulatory interactions. Another major direction of our research is to decipher “the cis-regulatory code”, i.e., to precisely describe how gene regulation is encoded in DNA as so-called regulatory elements. It is estimated that 90% of known disease-related mutations may be located in these regulatory elements, necessitating quantitative models that can accurately predict their function and the impact of mutations therein. In this talk, I will show how our analytical tools can provide experimentally testable hypotheses regarding regulatory networks and the function of regulatory elements, in the context of development, behavior, cancer progression, and drug response. I will also outline major directions for our future research.

Bio:

Saurabh Sinha received his Ph.D. in Computer Science from the University of Washington, Seattle, in 2002, and after post-doctoral work at the Rockefeller University with Eric Siggia, he joined the faculty of the University of Illinois, Urbana-Champaign, in 2005. He is Founder Professor and Willett Faculty Scholar in the Department of Computer Science, and Director of Computational Genomics in the Carl R. Woese Institute for Genomic Biology. His research is in the area of bioinformatics, with a focus on regulatory genomics and systems biology. Sinha is an NSF CAREER award recipient and has been funded by NIH, NSF and USDA. He co-directed an NIH BD2K Center of Excellence and is currently a thrust lead in the NSF AI Institute at UIUC. He leads the educational program of the Mayo Clinic-University of Illinois Alliance, and co-led data science education for the Carle Illinois College of Medicine. Sinha has served as Program co-Chair of the annual RECOMB Regulatory and Systems Genomics conference and is on the Board of Directors for the International Society for Computational Biology. He was a recipient of the University Scholar award of the University of Illinois, and selected as a Fellow of the AIMBE in 2018.

International Physical Internet Conference 2021 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 Logistics Nodes, Logistics Networks, System of Logistic Networks, Access and Adoption, Governance. 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.
 
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.

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

If you are interested in attending, please review the below information and register online.

SESSION OVERVIEW

We will dive deeper into using advanced supply chain analytics to optimize cost to service, inventory, and service level tradeoffs. This demo will show how o9’s complex platform uses performance analytics and data management combined with AI rule-based micro segmentation to provide prescriptive outputs to operational supply chain planning.

SESSION SPEAKER

Aseem is a Vice President of Product Management and Marketing  at o9. He has over 20 years of professional experience spanning Inventory Optimization, Network Design and Sales & Operations Planning. Over the years, he as worked with customers across multiple industries advising them on Inventory Planning, Service Parts Management, Network Design and Sales and Operations Planning. Aseem has a MS degree from The Ohio State University, Columbus and a Bachelor of Engineering from Punjab University, India.

The session will be moderated by Alan Erera, Associate Chair for Research and Co-Executive Director, Georgia Tech Panama Logistics Innovation & Research Center and UPS Professor of Logistics

Register Online for this upcoming SCL IRC seminar

Attendance is complimentary and this session is open to the public.

This course is the third in the four-course Supply Chain Analytics Professional certificate program. It introduces the field of machine learning, an area where algorithms learn patterns from data to support proactive decision making, as it applies to supply chain management. You’ll use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance. You’ll use Python and PowerBI to create and analyze regression, clustering, and classification models.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

• Understand the role of machine learning (ML) in Supply Chain Management (SCM)
• Apply advanced analytics techniques to build planning tools that can leverage large and real-time data sets
• Apply ML in demand forecasting and predictive maintenance
• Understand how to assess ML model performance, improve models, and pick the best model for a decision
• Use Python and PowerBI to build, analyze, and deploy ML models

What Is Covered

• How ML relates to SCM
• ML algorithms such as regression trees, clustering techniques, decision trees, random forests, logistic regression
• Aspects of ML projects including parameter tuning, cross validation, and assess model performance
• Application of ML in demand forecasting for sales and operations planning (S&OP) and inventory management
• Application of ML in predictive maintenance
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

This course is the fourth in the 4-course Supply Chain Analytics Professional certificate program. It incorporates learning advanced analytics and mathematical optimization to find solutions for supply chain problems. You’ll learn how to use linear programming, mixed integer programming, and heuristics to conduct prescriptive analytics related to production processes, distribution networks, and routing. The course serves as a capstone for the program by culminating in a hackathon where you’ll design networks, inventory policies, and scenarios and then evaluate the outcomes via simulations.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

• Use mathematical optimization to transform Supply Chain Management (SCM) processes.
• Apply LP, MIP, and heuristics to SCM, particularly in production planning, routing, and network design.
• Utilize PowerBI and Python in optimization projects.
• Participate in a hackathon that pulls together everything learned throughout the certificate program.

What Is Covered

• Role of mathematical optimization in addressing complex SCM challenges  
• Appropriate application of linear programming (LP), mixed integer programming (MIP), and heuristics
• Evaluation of production processes, distribution networks, and routes using optimization
• Ability to pull together all content of the certificate program into a prescriptive analytics project
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

Category Management and Sourcing Leadership 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

This course is the first in the four-course Supply Chain Analytics Professional certificate program. It prepares you to apply leading-edge analytical methods and technology enablers across the supply chain. You’ll learn the dynamics of supply chains, the most relevant planning challenges, and the roles of different types of analytics. Next, you’ll learn about data cleansing, exploratory data analysis, and visualization. You’ll use Python and PowerBI to analyze the causes of underperformance and to build dashboards to visualize supply chain data. You will leave knowing how to gather, analyze, and prepare your data through descriptive analytics before you dig into deeper applications.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

• Understand the most relevant planning challenges across the strategic, tactical, and operational levels of supply chains
• Learn the difference between analytics types, the links between them, and how to best use them to improve supply chain management (SCM) processes
• Use Key Performance Indicators (KPIs) to find causes of underperformance in supply chains and to plan for analytics projects that will address strategic SCM goals
• Utilize Python and PowerBI to understand, visualize, and analyze data in order to prepare for deeper analytics

What Is Covered

• The role of analytics in SCM
• Types of analytics (descriptive, diagnostic, predictive, and prescriptive) and the relationships between them
• Preprocessing (cleaning and integrating) data as it relates to SCM
• Conducting exploratory data analysis on supply chain data
• Best practices for visualizing data and building dashboards
• Identifying and analyzing KPIs of SCM
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

Essentials of Negotiations and Stakeholder Influence 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

This course is the second in the four-course Supply Chain Analytics Professional certificate program. It emphasizes operational performance metrics to align supply chain management with strategic business goals. You’ll learn several statistics concepts (e.g. variance analysis, hypothesis testing, forecasting methods) along with inventory management models. You’ll use diagnostic analytics with PowerBI and Python to conduct demand and service profiling, undertake root cause analysis, and use time series forecasting in inventory management.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

• Understand why and how to align Supply Chain Management (SCM) strategy with business strategy
• Learn statistics techniques as they relate to SCM
• Understand inventory management models and how to apply statistics techniques to them
• Create time series forecasts based on SCM data
• Utilize Python and PowerBI to perform statistical analyses, create time series forecasts and visualize results

What Is Covered

• The importance of aligning SCM and business strategy
• How to ask the right business questions as they relate to SCM
• How to use statistics to identify issues, compare data, and forecast decision outcomes
• Statistical concepts including variance analysis and hypothesis testing
• Inventory management models
• Applying statistics to inventory management models
• Forecasting techniques including time series forecasting
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

This course focuses on defining, executing, and improving the S&OP process. Participants will be introduced to the appropriate stakeholders of S&OP, the importance of S&OP to corporate performance, S&OP cadence, and the use of visionary technology to bring S&OP to the next level. Business cases will be used to show concrete examples of companies where S&OP is effectively applied.

WHO SHOULD ATTEND

• Chief Operating Officers, Supply Chain, Sales, Marketing and Finance Management Executives (Directors, VPs, EVPs)
• Supply Chain and Logistics Managers, Consultants, Supervisors, Planners, and Engineers
• Supply Chain Education and Human Resource Management personnel
• Inventory and Demand Planners
• Procurement and Sourcing Analysts and Managers
• Manufacturing Planners, Analysts, and Managers
• Sales Operations Managers, Analysts, Planners, Supervisors, Directors

HOW YOU WILL BENEFIT

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

• Understand the need for an S&OP cycle in a company
• Apply principles key to success of an S&OP process
• Experience true market examples relevant to their businesses

LEARNING OBJECTIVES

• Learn how to identify and apply best fit S&OP process and technology enablers to your organization and make it a reality based process.
• Walk through a complete simulated S&OP cycle supported by a technology enabler.
• Understand the interaction and integration between the financial and operation levels of S&O.
• Learn the key components of an effective S&OP business case through discussion of real life examples of how companies have benefited from the implementation of best practices in S&OP.

WHAT IS COVERED

• Defining the S&OP process before adopting technology
• The advantages of value based and reality based S&OP
• Why S&OP needs to be integrated closely with operational planning
• What is the scope of each role in the S&OP Cycle
• What are the most valuable outputs and results of the S&OP Cycle
• How can technology enable companies to take performance to the next level
• Experience a complete simulated technology-enabled S&OP Cycle

In today’s global economy, operating risks are increasingly on the minds of executives. The specific context of operating risk can range from general areas of business continuity to the effects of natural disasters. In this course participants will gain a solid understanding of Supply Chain Risk Management principals including effective ways to identify, mitigate and measure the impact of potential supply chain disruptions.

WHO SHOULD ATTEND

• Chief Operating Officers, Supply Chain, Sales, Marketing and Finance Management Executives (Directors, VPs, EVPs)
• Supply Chain and Logistics Managers, Consultants, Supervisors, Planners, and Engineers
• Supply Chain Education and Human Resource Management personnel
• Inventory and Demand Planners
• Procurement and Sourcing Analysts and Managers
• Manufacturing Planners, Analysts, and Managers
• Sales Operations Managers, Analysts, Planners, Supervisors, Directors

HOW YOU WILL BENEFIT

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

• Develop a broader, more comprehensive understanding of how their company’s supply chain may be at risk
• More effectively communicate to their company’s stakeholders the realities of supply chain risks
• Build a cross-functional understanding of the building blocks for an effective Supply Chain Risk Management to be used in their particular business
• Improve their company’s Supply Chain Risk Mitigation program

WHAT IS COVERED

• The difference between crisis management and supply chain risk management
• The significant long term impact of supply chain disruptions
• Why supply chain risk management activities require enterprise wide participation
• How companies can take proactive, actionable steps to add significant resiliency to their supply chain operation, often without requiring significant levels of investments

This course is the third in the four-course Supply Chain Analytics Professional certificate program. It introduces the field of machine learning, an area where algorithms learn patterns from data to support proactive decision making, as it applies to supply chain management. You’ll use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance. You’ll use Python and PowerBI to create and analyze regression, clustering, and classification models.

Who Should Attend

Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.

How You Will Benefit

• Understand the role of machine learning (ML) in Supply Chain Management (SCM)
• Apply advanced analytics techniques to build planning tools that can leverage large and real-time data sets
• Apply ML in demand forecasting and predictive maintenance
• Understand how to assess ML model performance, improve models, and pick the best model for a decision
• Use Python and PowerBI to build, analyze, and deploy ML models

What Is Covered

• How ML relates to SCM
• ML algorithms such as regression trees, clustering techniques, decision trees, random forests, logistic regression
• Aspects of ML projects including parameter tuning, cross validation, and assess model performance
• Application of ML in demand forecasting for sales and operations planning (S&OP) and inventory management
• Application of ML in predictive maintenance
• Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)

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.

Many Supply Chain decisions are concerned with the timely and efficient procurement, allocation, and distribution of resources (e.g. funds, supplies, volunteers, money, employees) through a supply chain network. This course will explore methodologies for “medium term” decision making including procurement and inventory policies, strategies for distribution and allocation of limited resources, and supply chain design.

What Is Covered

• Procurement decisions
• Inventory management techniques for a single event versus ongoing operations under uncertainty
• Strategies for resource allocation geographically and over time

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

Remote Learning courses taken individually (not the full 3 course certificate program) will be $1800 each rather than the original fee of $2400, All three Courses taken as a part of the Certificate Program are offered at a reduced cost of $1400 each.

In need of financial support? Scholarships may be available If you are working for an NGO or outside of the United States. Contact Program manager, Joscelyn j.cooper@isye.gatech.edu for further details.

Please Note: All three (3) courses that comprise the Health and Humanitarian Supply Chain Management Certificate have been transitioned to a Remote Learning format and will be taught by LIVE video instruction similar to the experience you would receive in person.

• Responsive Supply Chain Design and Operations May 10-13

• Inventory Management and Resource Allocation in Supply Chains May 17-20

• Systems Operations and Strategic Interactions in Supply Chains May 24-27

The remote learning experience is maximized through our blended delivery format, consisting of online pre-course modules, live interactive video instruction, games, and breakout groups. The program is spread over three weeks, running only for a few hours each day (9:30am to 1:00pm, US Eastern time Monday through Thursday) to provide flexibility with work schedules.

Meeting demand in a timely and cost-effective manner is important both in public and private supply chains, and heavily depend on the design and operation of these supply chains. Demand is affected by ongoing factors such as local economy, infrastructure, and geographic location, as well as unexpected events such as natural or manmade disasters or other large-scale disruptions. Designing and operating responsive supply chains requires the consideration of uncertainty in timing, scope, scale, and understanding of various topics such as forecasting, distribution network design, and inventory management. This course will examine methods and models for making supply chain design and operational decisions and explore the significant value that is obtained through informed decision-making in advance of an unpredictable event or long-term strategy for meeting the need of customers and beneficiaries.

What Is Covered

• Network modeling approaches
• Forecasting techniques
• Strategies for making decisions under uncertainty
• Other data-driven analytical approaches

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

Remote Learning courses taken individually (not the full 3 course certificate program) will be $1800 each rather than the original fee of $2400, All three Courses taken as a part of the Certificate Program are offered at a reduced cost of $1400 each.

In need of financial support? Scholarships may be available If you are working for an NGO or outside of the United States. Contact Program manager, Joscelyn j.cooper@isye.gatech.edu for further details.

Please Note: All three (3) courses that comprise the Health and Humanitarian Supply Chain Management Certificate have been transitioned to a Remote Learning format and will be taught by LIVE video instruction similar to the experience you would receive in person.

• Responsive Supply Chain Design and Operations May 10-13

• Inventory Management and Resource Allocation in Supply Chains May 17-20

• Systems Operations and Strategic Interactions in Supply Chains May 24-27

The remote learning experience is maximized through our blended delivery format, consisting of online pre-course modules, live interactive video instruction, games, and breakout groups. The program is spread over three weeks, running only for a few hours each day (9:30am to 1:00pm, US Eastern time Monday through Thursday) to provide flexibility with work schedules.

Often the lack of cooperation and coordination between organizations or stakeholders lead to inefficiencies, despite having common goals. A systems view is needed to ensure appropriate use of scarce resources to meet the multiple, and often conflicting, short- and long-term goals from multiple constituents. This course will focus on conceptual and modeling skills to understand and effectively manage supply chains and operations from a systems perspective. Models will address system characteristics (e.g., demand dependencies) that drive system dynamics and policies to regulate performance. Course topics include methods for improving coordination and collaboration, addressing demand dependencies, and reliably measuring and evaluating system performance.

What Is Covered

• Network modeling approaches
• Forecasting techniques
• Strategies for making decisions under uncertainty
• Other data-driven analytical approaches

Who Should Attend

This course is designed for representatives from governmental or non-governmental organizations, private corporations, military, and foundations, including but not limited to senior executives overseeing administrative and operational functions of an organization, logistics and supply chain managers, program managers, directors of field operations, directors of emergency/disaster preparedness and response, and public health professionals.

Remote Learning courses taken individually (not the full 3 course certificate program) will be $1800 each rather than the original fee of $2400, All three Courses taken as a part of the Certificate Program are offered at a reduced cost of $1400 each.

In need of financial support? Scholarships may be available If you are working for an NGO or outside of the United States. Contact Program manager, Joscelyn j.cooper@isye.gatech.edu for further details.

Please Note: All three (3) courses that comprise the Health and Humanitarian Supply Chain Management Certificate have been transitioned to a Remote Learning format and will be taught by LIVE video instruction similar to the experience you would receive in person.

• Responsive Supply Chain Design and Operations May 10-13

• Inventory Management and Resource Allocation in Supply Chains May 17-20

• Systems Operations and Strategic Interactions in Supply Chains May 24-27

The remote learning experience is maximized through our blended delivery format, consisting of online pre-course modules, live interactive video instruction, games, and breakout groups. The program is spread over three weeks, running only for a few hours each day (9:30am to 1:00pm, US Eastern time Monday through Thursday) to provide flexibility with work schedules.

Abstract: Due to the inherent complexity of neural models, relating the spiking activity of a network to its structure requires simplifying assumptions, such as considering models in the thermodynamic mean-field limit. In this last limit, an infinite number of neurons interact via vanishingly small interactions, thereby erasing the finite size geometry of interactions. To better capture the geometry in question, we analyze the activity of neural networks in the replica-mean-field limit regime. Such models are made of infinitely many replicas which interact according to the same basic structure as that of the finite network of interest. Our main contribution is an analytical characterization of the stationary dynamics of intensity-based neural networks with spiking reset and heterogeneous excitatory synapses in this replica-mean-field limit. Specifically, we functionally characterize the stationary dynamics of these limit networks via ordinary or partial differential equations derived from the Poisson Hypothesis of queuing theory. We then reduce this functional characterization to a system of self-consistency equations specifying the stationary neuronal firing rates. Of general applicability, our approach combines the rate-conservation principle from point-process theory and analytical considerations from generating-function methods. Such limits can be used for first-order models, whereby elementary replica constituents are single neurons with independent Poisson inputs, and in second-order models, where these constituents are pairs of neurons with exact pairwise interactions. In both cases, these replica-mean-field networks provide tractable versions that retain important features of the finite structure of interest. We validate our approach by demonstrating numerically that replica-mean-field models better capture the dynamics of feed-forward neural networks with large, sparse connections than their thermodynamic counterparts. We also illustrate the practical interest of this approach by analyzing some neuronal rate-transfer functions and by computing the correlation structure of certain pair-dominated network dynamics.

Joint work with T. Taillefumier.

Bio: F. Baccelli is Simons Math+ECE Chair at UT Austin and part time researcher at INRIA. His research directions are at the interface between Applied Mathematics and Communications. He is co-author of research monographs on point processes and queues, max plus algebras and network dynamics, stationary queuing networks, stochastic geometry and wireless networks. He received the France Télécom Prize of the French Academy of Sciences in 2002, the ACM Sigmetrics Achievement Award in 2014, the 2014 Stephen O. Rice Prize, and the 2014 Leonard G. Abraham Prize of the IEEE Communications Theory Society. He is a member of the French Academy of Sciences.

Bio:

Dr. Brandon J. Pitts is an Assistant Professor in the School of Industrial Engineering at Purdue University, West Lafayette, IN. Also, at Purdue, he is Director of the Next-generation Human-systems and Cognitive Engineering (NHanCE) Lab, Faculty Associate with the Center on Aging and the Life Course (CALC), and Co-Director of the FAA Center of Excellence for Technical Training and Human Performance (TTHP). He completed a B.S. in Industrial Engineering at Louisiana State University in 2010, and a M.S.E and Ph.D. in Industrial and Operations Engineering at the University of Michigan (UM), Ann Arbor, MI in 2013 and 2016, respectively. Prior to his faculty appointment, he was a Research Fellow in the UM Center for Healthcare Engineering and Patient Safety (CHEPS). Dr. Pitts’ research focuses on cognitive engineering, human-automation interaction, context-sensitive interface design, and gerontechnology in complex transportation and work environments, such as driving and aviation. His research has been funded by several government and industry sponsors, such as National Science Foundation, Department of Transportation, Federal Aviation Administration, and Ford Motor Company. Dr. Pitts is also a registered Engineer Intern (E.I.T).

If you are interested in attending, please review the below information and register online.

SESSION OVERVIEW

What exactly is a Control Tower? How do perturbation in supply, capacity, and demand affect decision in the near term horizon? And, how does it drive value to businesses? This lecture will dive deeper into the o9 platform to discuss how technology can turn supply chains into a prescriptive machine by linking demand opportunities and supply disruptions, such as shipment or production delays, with revenue, margin, and service level tradeoffs.

SESSION SPEAKER

Ripu Daman Singh is currently serving as a VP in Product Management and Marketing at o9 Solutions, responsible for multiple initiatives including Operational Planning / S&OE / Control Tower space. He has over 20 years of experience in developing software products solving supply chain problems. He has worked with fortune 500 companies across a diverse set of industries delivering packaged applications solving supply chain planning, data management, pricing, and execution software. He holds a Ph.D. in Operations Research and Industrial Engineering from Pennsylvania State University.

The session will be moderated by Tim Brown, Managing Director of the Supply Chain and Logistics Institute.

Register Online for this upcoming SCL IRC seminars

Attendance is complimentary and this session is open to the public.

Bio: Juba Ziani is a Warren Center Postdoctoral Fellow at the University of Pennsylvania, hosted by Sampath Kannan, Michael Kearns, Aaron Roth, and Rakesh Vohra. Prior to this, he was a PhD student at Caltech in the Computing and Mathematical Sciences department, where he was advised by Katrina Ligett and Adam Wierman.

Juba studies the optimization, game theoretic, economic, ethical, and societal challenges that arise from transactions and interactions involving data. In particular, his research focuses on the design of markets for data, on data privacy with a focus on "differential privacy", on fairness in machine learning and decision-making, and on strategic considerations in machine learning.

If you are interested in attending, please review the below information and register online.

SESSION OVERVIEW

We will discuss the top challenges and supply chain disruptions o9 is seeing in the market right now, especially in these post-COVID times. Umesh will then share how o9 is coming in and solving these problems by walking through specific customer stories and giving a high overview into what makes the o9 platform the most innovative next-generation Integrated Business Planning solution on the market.

SESSION SPEAKER

Umesh Arasu is an EVP of Product Management & Product Marketing at o9 Solutions. He has over 20 years of industry and research experience working with large fortune companies in Retail, Consumer Goods, Distribution and Manufacturing. He has a successful track record of bringing innovative enterprise applications to market in the areas of integrated business planning and business intelligence. Prior to o9, he was involved in strategic and leadership roles for enterprise platform and applications companies such as MicroStrategy (MSTR), Teradata (TDC), and i2 Technologies (now JDA / BY). His research interests include supply chain analytics, combinatorial optimization, data visualization and network flow algorithms.

Umesh has a Ph.D in Operations Research, M.S in Industrial & Manufacturing Systems Engineering from Kansas State University and a B.S in Mechanical Engineering.

The session will be moderated by Benoit Montreuil, Coca-Cola Material Handling & Distribution Chair and Director of the Physical Internet Center.

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Attendance is complimentary and this session is open to the public.

Abstract: Dr. Montague is an industrial engineer that specializes in human factors and ergonomics and health systems engineering. In this presentation she will describe the global challenges of increasing patient access to health care with a declining, burned out physician workforce. The goal of this research is to develop methods, models and tools to inform safe, effective and appropriate technologies to support patient and physician work. Research challenges related to understanding patient and physician needs in complex systems will be discussed through a series of mixed methods studies. Future research related to a post pandemic health care system and equity in systems engineering methods will be discussed.

Bio: Enid Montague received MS and PhD degrees in Industrial and Systems Engineering from Virginia Tech, specializing in human factors and ergonomics engineering. Dr. Montague is currently an Associate Professor in the college of computing at DePaul University and adjunct professor at Northwestern University’s Feinberg School of Medicine. She is the director of the Wellness and Health Enhancement Engineering Laboratory (WHEEL). Dr. Montague has received numerous awards for her research including the Francis Research Fellowship, a Kl2 early career award from the National Institutes of Health (NIH), and a Fulbright award to improve health care systems.

Bio:
Diego Cifuentes is an applied math instructor in the Massachusetts Institute of Technology (MIT). Previously he was a postdoctoral researcher at the Max Planck Institute for Mathematics in the Sciences, and before that he completed his Ph.D. in the Electrical Engineering and Computer Science department at MIT under the supervision of Pablo Parrilo. His research interests include mathematical optimization, computational algebraic geometry, and their applications in sciences and engineering.

Abstract: Over 300 billion dollars have been spent in online advertising markets during 2019, and this number is expected to grow to almost a trillion by 2025. Auctions are the standard method of selling ads in these markets; thus, optimizing them has ever-increasing importance. In this talk, I discuss my work on improving the performance of one of the most widely used auctions in these markets, the second price auction with reserve prices. This work focuses on optimizing personalized reserve prices with the goal of maximizing revenue. (The reserve price of each buyer is the minimum amount they should bid to be considered in the auction.) To optimize these reserve prices, I take a data-driven approach. That is, given a dataset of buyer’s submitted bids to a set of auctions, the goal is to find a single vector of reserve prices that maximizes the total revenue if used over all these auctions. This is indeed the approach taken by prominent advertising platforms, which generate a massive dataset of auctions daily. They then use this data to optimize the reserve prices for future auctions. 

The particular problem studied in this work is shown to be NP-hard, and the best-known approximation factor for that was 0.5 prior to our work. I present a polynomial-time algorithm with a significantly improved approximation factor of 0.68. I also discuss a more general algorithm that works for multi-unit auctions and obtains a 0.63-approximation.  Both algorithms are based on novel LP-formulations and rounding procedures, which might be of independent interest.

Bio: Mahsa is a fifth-year Ph.D. candidate in Computer Science at the University of Maryland, advised by MohammdTaghi Hajiaghayi. During her Ph.D. Mahsa has spent a year as an intern at Google and Microsoft Research, and two semesters as a visiting student and Simons Institute for the theory of computing at UC Berkeley. Her main area of research is algorithmic mechanism design with a special focus on designing models and algorithms for online platforms such as online advertising markets, online matching markets, and online retail markets. She is also interested in the design and analysis of big-data algorithms, particularly in distributed and dynamic settings. Mahsa’s research is supported by the Ann G. Wylie Dissertation Fellowship and the 2020 Google Ph.D. Fellowship for Algorithms, Optimizations, and Markets.

Abstract: This talk explores the value of autonomous vehicles in mitigating the challenges of last-mile delivery across urban to rural settings. The Capacitated Autonomous Vehicle Assisted Delivery Problem (CAVADP) uses an autonomous vehicle to pick up and drop off a delivery person, avoiding the need to find parking. The analysis begins by consider a complete grid of customers, a reasonable assumption for urban environments where the time to find parking is expected to be high. I characterize the optimal solution based on the number of customers, driving speed of the vehicle, walking speed of the delivery person, and the time for loading packages. I present a polynomial-time algorithm for the CAVADP on a complete grid. To consider other customer geographies, I model the CAVADP as a mixed integer program on a general graph. To solve this problem to optimality, model improvements are necessary to control the number of variables and the size of the branch-and-bound tree. I exploit the structure of the optimal solution to develop preprocessing techniques and valid inequalities. To build insights across urban to rural settings, I present a case study to show the value of using autonomous vehicles relative to standard delivery with parking. Autonomous vehicle assisted delivery leads to increased productivity for the delivery person in all customer geographies. In particular, a delivery person saves more time on the delivery tour in urban environments than rural environments.

Bio: Sara Reed is a Ph.D. candidate in the Applied Mathematical and Computational Sciences interdisciplinary Ph.D. program at the University of Iowa. Her research has been conducted in the Department of Business Analytics in the Tippie College of Business under the supervision of Professor Ann Campbell and Professor Barrett Thomas. She will defend her dissertation in Spring 2021. Her research interests are in transportation logistics. Her dissertation focuses on determining the value of autonomous values in last-mile delivery. In addition, she has research interests in food rescue operations. Sara is a member of the Institute for Operations Research and the Management Sciences (INFORMS) and was the president of the INFORMS Iowa Student Chapter that received the Cum Laude Award in 2020.  This year, she received first place in the Bayer Women in Operations Research scholarship from the INFORMS Analytics Society. Sara obtained a Certificate in Graduate Teaching from the University of Iowa. She received her MS in Mathematics from the University of Iowa and her BA in Mathematics and Economics/Finance from Simpson College.

Optimizing group-level food policy interventions

Abstract:

The federal government currently spends over $100 billion per year on interventions aimed at increasing fruit and vegetable consumption among low income households. These include interventions related to price, nutrition education, and access. Currently, funds are allocated to each type of intervention in a siloed fashion, in some cases resulting in surprisingly disappointing outcomes. The goal of this work is to increase the efficacy of food policy interventions through optimization and increased personalization. This work introduces a novel consumer behavioral model for grocery shopping dynamics, which is nested into a bi-level model for optimizing the government's investments. In this model, the government’s goal is to increase fruit and vegetable (FV) consumption among low income households by utilizing strategic portfolios of interventions (referred to as intervention bundles). However, complete personalization may be undesirable or infeasible. Therefore, group-level personalization—where individuals are assigned to groups that receive unique intervention bundles—is considered. This work develops a new framework that allows us to quantify the level of personalization (i.e., the number of groups) needed to achieve a certain outcome level. We also show how this framework is generalizable to many settings beyond food policy.

Bio:

Elisabeth Paulson is a fifth-year Ph.D. candidate at the MIT Operations Research Center, where she is co-advised by Prof. Retsef Levi and Prof. Georgia Perakis. She is broadly interested in data-driven policy making and the design of public interventions for social good. Her current research focuses on supply chain and public policy interventions for creating better access to, and consumption of, fresh food. Before coming to MIT, she received a B.S. in Mathematics, B.S. in Statistics, and M.A. in Mathematics from the Pennsylvania State University. Elisabeth also spent a year working as a data scientist for Booz Allen Hamilton, and spent the summer of 2019 as an intern with IBM Food Trust. Elisabeth’s research is supported by the NSF Graduate Research Fellowship.

Georgia Tech Supply Chain Students
Please plan on joining us for Virtual Supply Chain Day! Visit https://www.scl.gatech.edu/supplychainday/students to learn how your can participate online.

Interested Organizations/Recruiters

If you are a organization who would like to participate, please email event@scl.gatech.edu.

The event will host supply chain representatives from ​​supply chain and logistics companies who will be on online to educate supply chain students about their organizations, available employment, internships, and capstone project opportunities.

We strongly encourage students to act now to seek full-time employment, internships, and projects (rather than waiting until the end of the semester).

For more information relating to the session, please visit https://www.scl.gatech.edu/supplychainday.