A Unifying Framework for the Capacitated Vehicle Routing Problem Under Risk and Ambiguity
A Unifying Framework for the Capacitated Vehicle Routing Problem Under Risk and Ambiguity
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DOI：
10.1287/opre.2021.0669
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
S. Ghosal;C. Ho;W. Wiesemann
中科院分区：
文献类型：
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作者：
S. Ghosal;C. Ho;W. Wiesemann
新框架统一了风险和模糊条件下的能力约束车辆路径问题在题为《风险和模糊条件下的能力约束车辆路径问题的统一框架》的研究中，作者提出了一个全面和通用的框架来解决能力约束车辆路径问题(CVRP)中需求不确定性带来的挑战。该框架能够考虑并结合各种风险度量、满意度量和负效用函数，提供了一种统一的方法来处理不确定条件下的CVRP的不同变体。通过在风险和模糊情况下提供对CVRP的统一处理，该框架使决策者能够优化路由决策，并考虑到相关的风险和不确定性。该框架的主要优势之一是其实现的实用性。证明了现有的分枝割算法只需最小的修改就能有效地求解受不确定性影响的CVRP问题的所有变种。这种可伸缩性和适应性使得该框架可以在实际环境中使用。
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.