Title: Resource Pooling in Hyperconnected Logistics Networks

Date: Monday, November 16, 2026
Time: 2:00 PM – 4:00 PM ET
Location: Virtual via Microsoft Teams
Meeting link: https://teams.microsoft.com/meet/297219506494396?p=jBxsWTUiavcTjnCxMG (Meeting ID: 297 219 506 494 396, Passcode: cp9zJ6Ty)

Yujia Xu
Machine Learning PhD Student
H. Milton Stewart School of Industrial and Systems Engineering
Georgia Institute of Technology

Committee
1 Dr. Benoit Montreuil (Advisor), H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
2 Dr. Walid Klibi, Centre of Excellence for Supply Chain Innovation and Transportation, Kedge Business School
3 Dr. Yao Xie, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
4 Dr. Valerie Thomas, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology
5 Dr. Mathieu Dahan, H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology

Abstract
Hyperconnected logistics networks comprise interconnected hubs and organizations whose resources can be shared, so that capacity committed at one location can serve demand arising elsewhere in the network. Resource pooling therefore extends capacity planning under uncertainty beyond individual facilities and firms to include the coordination of shared resources and the discovery of outside capacity. This dissertation develops stochastic optimization, decomposition, and learning methods for resource-pooling decisions, from deploying known resources to expanding the pool beyond the network. We first formulate the dynamic deployment of pooled workers and autonomous mobile robots across urban parcel hubs as a rolling-horizon two-stage stochastic program that respects worker shift and location preferences, and solve large-scale instances with an accelerated Benders decomposition and clustering-based relax-and-fix heuristics. Then we design battery swapping and charging networks that support long-haul truck electrification by keeping swapping close to freight demand while allowing charging to shift to grid-suitable hubs. We model this design problem as a multi-period, two-stage stochastic location–inventory–fleet program and solve it with a Lovász-cut Benders decomposition that exploits the submodularity of the battery recourse function. Lastly, we introduce open capacity pooling, in which a disrupted network discovers capacity held by previously unengaged providers through a closed loop of coordination, search, and admission under a limited budget of requests for quotation. Each request targets a capacity gap remaining after incumbent firms coordinate through the alternating direction method of multipliers (ADMM) without sharing their operating models, and is directed to a provider ranked by a machine-learning layer built on directory records and large language model readings of provider self-descriptions. We evaluate the proposed methods in computational studies grounded in real-world data from industry partners and public sources. The findings show how resource pooling helps logistics networks respond to uncertain demand and disruptions by coordinating shared resources and incorporating capacity from outside providers.