An Improved Ant Colony Algorithm for Vehicle Routing Problem with Workload Balance

被引：1

作者：

Fang, Yaohuiqiong ^{[1
]}

Li, Jingjing ^{[1
]}

机构：

[1] South China Normal Univ, Sch Comp Sci, Guangzhou 510631, Peoples R China

来源：

COMPUTER SUPPORTED COOPERATIVE WORK AND SOCIAL COMPUTING, CHINESECSCW 2021, PT I | 2022年 / 1491卷

关键词：

Vehicle routing problems; Workload balancing; Ant colony algorithm;

D O I：

10.1007/978-981-19-4546-5_41

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper studies a bi-objective vehicle routing problem considering both travel cost and workload balance. Different from the existing research, the workload balance is calculated by workload variance and average value. We propose an improved multi-objective ant colony algorithm (MO-ACOTC) to eliminate distorted solutions and use a new global pheromone update strategy. The algorithm also designs a travel constraint mechanism to dynamically control the workload for each vehicle. The experimental results on traditional and revised instances show that the algorithm can obtain satisfactory solutions.

引用

页码：529 / 540

页数：12

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