Thesis Title: Hyperconnected Parcel Logistics: Planning and Assessment  

Thesis Committee: 

Dr. Benoit Montreuil (advisor), School of Industrial and Systems Engineering, Georgia Institute of Technology 

Dr. Martin Savelsbergh (co-advisor), School of Industrial and Systems Engineering, Georgia Institute of Technology 

Dr. Leon McGinnis, School of Industrial and Systems Engineering, Georgia Institute of Technology 

Dr. Uday Venkatadri, School of Industrial Engineering, Dalhousie University 

Dr. Walid Klibi, Supply Chain Center of Excellence, Kedge Business School  

Date and Time: Friday, August 25, 2023, 10 am - 12 pm EST 

Online Meeting Link: Click Here to Join the Meeting  

Abstract: 

Today's last-mile logistics faces numerous challenges, particularly concerning cost management and meeting customers' escalating expectations. With the steady expansion of e-commerce, the volume of last-mile deliveries has surged, consequently driving up costs for logistics providers. Additionally, the competitive landscape has intensified, with businesses striving to offer same-day or even on-demand deliveries to cater to customers' demands for convenience and satisfaction. This thesis investigates pivotal facets of megacity parcel logistics, with a specific emphasis on two main objectives: (1) realizing the cost-saving potential of package consolidation and containerization, and (2) analytically assessing novel logistical network configurations that revolutionize the handling of packages within the logistics network. 

In Chapter 2, we present a formal definition of containerized consolidation in megacity parcel logistics and explore its potential benefits, including reductions in total handling and transit costs. We propose an Integer Programming (IP) formulation and conduct an extensive sensitivity analysis across diverse network configurations and demand patterns. The findings showcase the potential for remarkable savings, with up to 80% reduction in handling costs and over 20% reduction in total in-transit costs through the implementation of containerized consolidation.  

In Chapter 3, we shift our focus from tactical containerized consolidation planning to a dynamic and data-driven approach tailored to the fast-paced last-mile delivery environment. We present a Mixed Integer Programming (MIP) formulation for decentralized and dynamic consolidation and containerization of packages at distribution hubs. Given the complexity of this model, solving it in real-time dynamic scenarios is impractical. To tackle this challenge, we introduce two heuristic approaches to handle dynamic decisions and assess their performance against the optimal solution. Our findings demonstrate that the proposed heuristics can achieve nearly optimal solutions while considerably reducing computational time. 

Finally, In Chapter 4, we adopt a more holistic perspective on megacity parcel logistics, evaluating the advantages of the recently introduced and innovative Hyperconnected Logistic Web concept for enhancing urban parcel logistics efficiency and responsiveness. We emphasize the significance of a holistic approach in validating such solutions and introduce an agent-based discrete-event simulator platform, capable of modeling urban delivery networks at the parcel granularity. This simulator adeptly handles a range of strategic, tactical, and operational decisions necessary for urban logistic operations. Utilizing the proposed model and given real data from a high-profile package delivery company, we execute two sets of experiments to evaluate the effect of different package routing and consolidation strategies on logistics network performance. Preliminary results illustrate that a higher level of interconnection among nodes in the lower network tiers leads to reduced in-transit costs, enabling the provision of tighter customer delivery services. Furthermore, the initial experiments with the consolidation heuristics introduced in Chapter 3, demonstrate that the proposed heuristic yields lower operational costs compared to other traditional consolidation approaches.