Robust cooperative hub location optimization considering demand uncertainty and hub disruptions

Abstract

Amidst the rise of economic globalization and increased commodity trading, the logistics industry is experiencing rapid growth, encountering intricate transportation demands and fierce market competition. Simultaneously, it faces challenges related to infrastructure development and resource allocation efficiency. To enhance the adaptability and robustness of transportation networks when facing uncertain demands and potential hub disruptions, this paper proposes a two-stage robust optimization model for cooperative hub location problem. The model utilizes a hybrid algorithm that effectively combines the global search capability of genetic algorithms with the step-by-step optimization efficiency of Benders decomposition. Case study analyses demonstrate that, irrespective of the uncertainty environment, the cooperative model maintains lower total costs. Particularly in the case of large demand fluctuations, its cost advantages over non-cooperative models become notably prominent, showcasing remarkable performance in meeting service demands and enhancing resource utilization. Additionally, in cooperative networks, hub disruptions have a more significant impact on hub location decisions, with penalty and supplementary costs further exacerbating this influence. These research findings offer crucial insights for management practices: in uncertain market environments, adopting cooperative and robust planning strategies is pivotal for mitigating operational risks. When making decisions on cooperative hub location selection, carriers should comprehensively consider the interaction between uncertainty and economic benefits to achieve an optimal balance between risk and cost, ensuring the sustained economic feasibility of cooperative ventures.