Budget-Driven Multiperiod Hub Location: A Robust Time-Series Approach
针对多期枢纽选址问题中不确定且跨期相关的时间序列需求，提出嵌套模糊集和预算驱动模型，在约束每期期望成本的同时最大化稳健性水平，并给出有限样本性能保证。
A Robust Time-Series Approach to the Budget-Driven Multiperiod Hub Location Problem The (un)capacitated multiperiod hub location problem involves uncertain time-series demands, the distribution of which is unobservable and correlated over periods. In “Budget-Driven Multiperiod Hub Location: A Robust Time-Series Approach,” Hu, Chen, and Wang develop a nested ambiguity set that characterizes uncertain periodic demands via a general multivariate time-series model and propose a budget-driven model that constrains each expected periodic cost within a budget while optimizing the robustness level by maximizing the size of the nested ambiguity set. Under certain regularity conditions on the underlying VAR(p) or VARMA(p,q) process of the stochastic demand, the developed ambiguity set enjoys the desirable property of measure concentration that translates to finite-sample performance guarantees of optimization problems’ decisions. In the uncapacitated case, the proposed budget-driven model essentially optimizes a Sharpe ratio–type criterion over the worst case among all periods. This research contributes to time-series predictive distributionally robust optimization.