A Unifying Framework for the Capacitated Vehicle Routing Problem Under Risk and Ambiguity
提出一个统一框架，整合多种风险度量与满意度度量，解决需求不确定下的容量车辆路径问题，并展示现有算法可高效求解各变体。
New Framework Unifies Capacitated Vehicle Routing Problem Under Risk and Ambiguity In the study titled “A Unifying Framework for the Capacitated Vehicle Routing Problem Under Risk and Ambiguity,” the authors propose a comprehensive and versatile framework that addresses the challenges posed by demand uncertainty in the capacitated vehicle routing problem (CVRP). This framework is able to consider and incorporate various risk measures, satisficing measures, and disutility functions, providing a unified approach to tackle different variants of the CVRP under uncertainty. By offering a unified treatment of the CVRP under risk and ambiguity, this framework enables decision makers to optimize routing decisions, accounting for the associated risks and uncertainties. One of the key advantages of this framework is its practicality for implementations. The authors demonstrate that an existing branch-and-cut algorithm can effectively solve all variants of the uncertainty-affected CVRP with minimal modifications. This scalability and adaptability make the framework applicable in practical settings.