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Solving a stochastic facility location/fleet management problem with logic-based Benders' decomposition

机译:通过基于逻辑的Benders分解解决随机设施位置/车队管理问题

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摘要

This article addresses a stochastic facility location and vehicle assignment problem in which customers are served by full return trips. The problem consists of simultaneously locating a set of facilities, determining the vehicle fleet size at each facility, and allocating customers to facilities and vehicles in the presence of random travel times. Such travel times can arise, for example, due to daily traffic patterns or weather-related disturbances. These various travel time conditions are considered as different scenarios with known probabilities. A stochastic programming with bounded penalties model is presented for the problem. In order to solve the problem, integer programming and two-level and three-level logic-based Benders' decomposition models are proposed. Computational experiments demonstrate that the Benders' models were able to substantially outperform the integer programming model in terms of both finding and verifying the optimal solution.
机译:本文解决了随机设施位置和车辆分配问题,在该问题中,全程回程为客户提供服务。问题包括同时定位一组设施,确定每个设施的车队规模,以及在存在随机行驶时间的情况下将客户分配给设施和车辆。例如,由于日常交通方式或与天气有关的干扰,可能会导致此类旅行时间增加。这些各种旅行时间条件被认为是概率已知的不同场景。针对该问题,提出了一种带惩罚的随机规划模型。为了解决这个问题,提出了整数规划以及基于两层和三层逻辑的Benders分解模型。计算实验表明,在寻找和验证最优解方面,Benders模型能够大大胜过整数规划模型。

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