Improved ant colony optimization algorithm for solving vehicle routing problem with soft time windows

被引：0
|
作者：
He M. [1 ]
Wei Z. [1 ]
Wu X. [1 ]
Peng Y. [2 ]
机构：
[1] School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang
[2] School of Management, Jiangsu University, Zhenjiang
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2023年 / 29卷 / 03期
关键词：
ant colony optimization algorithm; soft time windows; variable neighborhood search; vehicle routing problem;
D O I：
10.13196/j.cims.2023.03.029
中图分类号：
学科分类号：
摘要：
To solve the Vehicle Routing Problem with Soft Time Window (VRPSTW) problem, a mixed integer programming model with distribution cost as the optimization objective was established, and an Improved Ant Colony Optimization (IACO) algorithm was proposed. Based on the traditional Ant Colony Optimization (ACO) algorithm, the formula of ant state transition probability was improved, and the pheromone update strategy was develped by adaptively adjusting the pheromone volatilization coefficient. The insertion operator and the exchange operator were designed to embed the variable neighborhood local search. The conditions for starting and exiting the local search were set to update the current local optimal solution. Three different scale examples in the Solomon benchmark set were selected to test the performance of the algorithm. The type-C instances with 100 customers in Solomon benchmark set were selected to verify the feasibility of this algorithm to solve large- scale problems. The results of IACO were compared with those of traditional ACO and other literatures. The experimental results showed that the optimization ability of IACO was better than other algorithms, and the optimal distribution scheme obtained by IACO achieved lower vehicle distribution cost. Therefore, the effectiveness of IACO was verified. © 2023 CIMS. All rights reserved.
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页码：1029 / 1039
页数：10
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