Solving a multi-objective dynamic stochastic districting and routing problem with a co-evolutionary algorithm

Solving a multi-objective dynamic stochastic districting and routing problem with a co-evolutionary algorithm
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用协同进化算法解决多目标动态随机分区和路由问题

DOI:
10.1016/j.cor.2015.09.002
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发表时间:
2016-03
影响因子:
4.6
通讯作者:
Laporte Gilbert
Laporte Gilbert
中科院分区:
工程技术2区
文献类型:
--
作者:
Lei Hongtao;Wang Rui;Laporte Gilbert

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本研究考虑一个多目标的动态随机分区和路由问题,其中客户的领土stochemical演变在几个时期的规划视野,并在服务车辆的数量,紧凑的地区,地区的相异性措施和公平的车辆利润被认为是目标。该问题被建模和求解为一个两阶段的随机规划,其中在每个时期,分区决策在第一阶段,和Beardwood-Halton-Hammersley公式被用来近似每个地区的期望路由成本在第二阶段。提出了一种改进的多目标进化算法(MOEA),提出了基于交配约束的偏好协同进化算法。该算法进行了测试,随机生成的实例,并与两个国家的最先进的MOEA。计算结果证实了该算法的优越性和有效性。此外,选择一个优选的设计所提出的问题的程序进行说明。
This study considers a multi-objective dynamic stochastic districting and routing problem in which the customers of a territory stochastically evolve over several periods of a planning horizon, and where the number of service vehicles, the compactness of the districts, the dissimilarity measure of the districts and an equity measure of vehicles profit are considered as objectives. The problem is modeled and solved as a two-stage stochastic program, where in each period, districting decisions are made in the first stage, and the Beardwood–Halton–Hammersley formula is used to approximate the expected routing cost of each district in the second stage. An enhanced multi-objective evolutionary algorithm (MOEA), i.e., the preference-inspired co-evolutionary algorithm using mating restriction, is developed for the problem. The algorithm is tested on randomly generated instances and is compared with two state-of-the-art MOEAs. Computational results confirm the superiority and effectiveness of the proposed algorithm. Moreover, a procedure for selecting a preferred design for the proposed problem is described.
DOI: 10.1109/cec.2002.1004406
发表时间: 2002-05
期刊: Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
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