Location for Profit Maximization: Applications to On-Demand Warehousing and Pilotless Air Cargo Transportation

Abstract

Modern logistics systems face increasing complexity due to the rapid growth of e-commerce and emerging technologies, particularly in shared warehousing platforms and air freight delivery. Accordingly, advanced approaches are needed to effectively address large-scale network design problems that prioritize profit-oriented objectives. This dissertation develops mathematical models and effective solution methodologies to optimize profitability in on-demand warehousing and pilotless air cargo transportation networks. In particular, the thesis introduces decomposition methods and heuristic algorithms to solve facility location problems arising in these applications with a focus on profit-maximization. First, a Lagrangian relaxation framework combined with a local search heuristic is used to solve a multi-period profit-maximizing facility location model in on-demand warehousing, accounting for price-sensitive demand and dynamic allocation. Next, for pilotless air cargo transportation, two network design models are developed and solved using a branch-and-price and genetic algorithm enhanced with score-based labeling and a hybrid heuristic to optimize business profit, hub placement, and airplane routing. The findings provide practical insights for enhancing profitability and operational responsiveness in both on-demand warehousing and pilotless air cargo logistics.