车辆路径问题
计算机科学
趋同(经济学)
人口
数学优化
布线(电子设计自动化)
构造(python库)
集成学习
过程(计算)
进化算法
算法
机器学习
人工智能
数学
社会学
计算机网络
程序设计语言
经济
人口学
操作系统
经济增长
作者
Feng Wang,Fanshu Liao,Yixuan Li,Xuesong Yan,Chen Xu
标识
DOI:10.1016/j.cie.2021.107131
摘要
The Vehicle Routing Problem (VRP) is a typical combinatorial optimization problem and has been studied for many years. However, there are few researches on the Dynamic Vehicle Routing Problem with Time Window (DVRPTW), which is an extension of VRP and more challenging with changing environmental factors, such as stochastic customer requests. Once changes happen, the routes should be adjusted for the new environments. In this paper, we construct a multi-objective optimization model for the DVRPTW and propose a new algorithm named as EL-DMOEA, where an ensemble learning method is investigated to improve the performance of the algorithm. In EL-DMOEA, to enhance the population’s diversity and accelerate the convergence, three different strategies, i.e., population-based prediction strategy, immigrant strategy and random strategy, are employed in the training process of three kinds of basic models respectively. The experimental results on the test benchmarks reveal that the proposed algorithm is effective to make promising routing plans.
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