Genetic algorithm for the vehicle routing problem with time windows and fuzzy demand

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) Pub Date : 2008-06-01 DOI:10.1109/CEC.2008.4631360

Jian Xu, G. Goncalves, T. Hsu

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引用次数: 9

Abstract

This paper considers a VRP with soft time windows and fuzzy demand (VRPTWFD). The objective is to minimize both the total distance covered by all vehicles as well as the sum of lateness at the customerpsilas due to the violation of time windows. This VRPTWFD is formulated as a two stages recourse model in the context of stochastic programming. The goal is then to minimize the expected cost, which includes the initial cost of the solution found in first stage and the additional cost due to the route failure in second stage. The theory of possibility is applied in the capacity constraint. In addition, a route failure estimation method is proposed to evaluate the additional cost as well as the expected cost. A genetic algorithm, in which a simulation phase based on sampling scenarios to evaluate the fitness of chromosome, is specifically designed to solve the two stages recourse model for the VRPTWFD. Finally an experimental evaluation of this developed algorithm is validated on a few VRPTWFD modified from the Solomon benchmarks.

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具有时间窗和模糊需求的车辆路径问题的遗传算法

本文研究了一种带有软时间窗和模糊需求的VRP (VRPTWFD)。目标是最小化所有车辆行驶的总距离，以及由于违反时间窗口而在客户端迟到的总和。在随机规划的背景下，将VRPTWFD表述为两阶段追索模型。然后，目标是最小化预期成本，其中包括在第一阶段找到的解决方案的初始成本和在第二阶段由于路由失败而导致的额外成本。在容量约束中应用了可能性理论。此外，还提出了一种路由故障估计方法，以评估额外成本和期望成本。针对VRPTWFD的两阶段追索模型，设计了一种基于采样场景的模拟阶段评估染色体适应度的遗传算法。最后，在Solomon基准改进后的几个VRPTWFD上对该算法进行了实验验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

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