Budget-Driven Multiperiod Hub Location: A Robust Time-Series Approach

• 成果类型: Article
• 署名作者: Hu, Jie; Chen, Zhi; Wang, Shuming
• 署名单位: Beijing Jiaotong University; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Chinese University of Hong Kong
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.2022.0319
• 发表日期: 2025
• 关键词: distributionally robust; Facility Location; decision-making; optimization; uncertainty; projects; service; network

摘要

We study the (un)capacitated multiperiod hub location problem with uncertain periodic demands. With a distributionally robust approach that considers time series, we build a model driven by budgets on periodic costs. In particular, we construct a nested ambiguity set that characterizes uncertain periodic demands via a general multivariate time-series model, and to ensure stable periodic costs, we propose to constrain each expected periodic cost within a budget whereas optimizing the robustness level by maximizing the size of the nested ambiguity set. Statistically, the nested ambiguity set ensures that the model's solution enjoys finite-sample performance guarantees under certain regularity conditions on the underlying VAR(p) or VARMA(p, q) process of the stochastic demand. Operationally, we show that our budget-driven model in the uncapacitated case essentially optimizes a Sharpe ratio-type criterion over the worst case among all periods, and we discuss how cost budgets would affect the optimal robustness level. Computationally, the uncapacitated model can be efficiently solved via a bisection search algorithm that solves (in each iteration) a mixed-integer conic program, whereas the capacitated model can be approximated by using decision rules. Finally, numerical experiments demonstrate the attractiveness and competitiveness of our proposed model.