Theory and Practice of Large-scale Logistics: Offline Contextual Bandits and Decomposition Methods

This dissertation addresses two important problems in large-scale logistics, motivated by first-hand experience in industry and military settings. The first part focuses on offline contextual bandits, drawing from work at Uber on driver incentive programs. I introduce Empirical Soft Regret (ESR), a novel loss function for value-based learning that addresses limitations of accuracy-based approaches in misspecified settings. Unlike standard methods that fail when reward models are poorly specified, ESR provably yields policies that asymptotically achieve optimal performance while remaining compatible with gradient-based optimization. The value of this approach is demonstrated through applications in health datasets, news recommendation, and computational materials science. The second part addresses large-scale logistics involving simultaneous routing and scheduling of commodity deliveries across intermodal networks. In collaboration with the United States Marine Corps and Navy, I develop a mixed-integer programming formulation for expeditionary warfare logistics that captures the various physical constraints placed on the network. To address computational limitations, I propose an efficient solution method based on dual decomposition that leverages Lagrangian duality to split the problem into smaller, computationally tractable subproblems. This work bridges the gap between operations research theory and practice, demonstrating how theoretical foundations can be successfully translated into practical solutions for complex real-world logistics challenges.

Name thesis:degree_name

Ph. D., Operations Research and Information Engineering

Level thesis:degree_level

Doctor of Philosophy

Discipline thesis:degree_discipline

Operations Research and Information Engineering

Grantor

Cornell University

Year dc:date.issued

2025

Author dc:creator

• Tan, Samuel

Committee members dc:contributor.committeemember

• Kallus, Nathan
• Henderson, Shane

• Contextual Bandits
• Dual Decomposition
• Large-scale Logistics

Statement dc:rights

• Attribution 4.0 International

Licence dc:rights.uri

• https://creativecommons.org/licenses/by/4.0/

Language dc:language.iso

en

DOI

https://doi.org/10.7298/rcrq-xt69

Dc Identifier Other

ProQuest Submission ID: 15284
ProQuest Publication ID: 32242499

OAI identifier oai:identifier

oai:ecommons.cornell.edu:1813/121112