题    目：The Voice of Fairness in Humanitarian Logistics

嘉　宾：王曙明 教授 中国科学院大学

主持人：曾智宇 助理教授 上海交通大学安泰经济与管理学院

时　间：2026年10月14日（周三）10:00-11:30

地　点：上海交通大学安泰经济与管理学院包兆龙图书馆A507

内容简介：

The increasing frequency of natural disasters poses unprecedented challenges to humanitarian relief networks. Worse still, inaccurate budgeting and demand misestimation in pre-disaster planning often exacerbate post-disaster inequitable outcomes. We study a humanitarian relief prepositioning problem under demand uncertainty, where a decision maker determines facility locations and inventory levels subject to a fairness-aware budget. The problem is formulated as a predictive Wasserstein distributionally robust model, with the ambiguity set constructed from available data and a demand prediction model. A key novelty of this study is a far-sighted and unified criterion, termed the ''Voice of Fairness'' (VoF), which ''speaks up'' for the relative importance of fairness versus operational expenditure. The VoF admits an insightful representation that coherently integrates the budget, predicted expected operational cost, unfairness, and ambiguity effect. By maximizing the VoF, the decision maker can transparently navigate the optimal fairness-cost tradeoff within the budget. The structure of the VoF permits a convex reformulation of the second-stage fairness-aware transportation problem, thereby allowing the model to be solved efficiently via bisection search. Sensitivity analysis shows that, with the fixed predicted distribution}, the finite optimal VoF is increasing in budget, decreasing in ambiguity radius, and convex in each parameter, offering clear guidance to decision makers: A larger budget or smaller ambiguity radius can mitigate predicted unfairness and expand the solution space for a more favorable fairness vs. operational cost balance. Finite-sample guarantees are further established for the out-of-sample VoF under a Wasserstein-measured distribution shift; convergence to the true optimum is recovered in the no-shift case. Numerical experiments on the Yushu earthquake and Super Typhoon Karding demonstrate the practical value of our approach: Through the lens of VoF, our budgetary framework reduces expected unfairness (e.g., by 65.16\%) and fairness-aware total cost (e.g., by 11.33\%) with moderate operational cost increase (e.g., by 5.69\%); incorporating contextual information further mitigates unfairness (e.g., by 68.79\%), and accounting for ambiguity reduces budget-constraint violation (e.g., by 57.14\%).

演讲人简介：

Dr. Shuming Wang is a Professor of Management Science at the School of Economics and Management, University of Chinese Academy of Sciences (UCAS). His research interests include predictive data-driven analytics with applications in location science, transportation, supply chain management, and healthcare operations. He has published more than 30 papers including those in Operations Research, Production and Operations Management, INFORMS Journal on Computing, and Transportation Science. He serves as an Area Editor at Computers & Operations Research and an Associate Editor at Decision Sciences.

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