Learning Ant Colony Algorithm for Green Multi-depot Vehicle Routing Problem

Authors

Hu Rong, 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; ;2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China;
Wenbo Chen, 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; ;2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China;
Bin Qian, 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; ;2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, China;
Guo Ning, 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; ;
Fenghong Xiang, 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China; ;

Abstract

Abstract: Considering the congested roads situation of urban central areas in China, a speed calculation method based on vehicle driving sections is designed, and a model for the Green Multi-Depot Vehicle Routing Problem with Total Fuel-Consumption cost Criterion (TFC-GMDVRP) is established, considering simultaneously the vehicle travel distance, load, and speed factors. A learning ant colony optimization algorithm (LACO), combining a knowledge model and an ant colony optimization algorithm (ACO), is proposed for solving the TFC-GMDVRP. In order to improve the performance and robustness of the algorithm's global search, the parameter knowledge that contains the different parameter combinations of ACO and the selection probability of each parameter combination is designed to adjust the ACO's parameters for each generation. In order to enhance the ability of algorithm's local search, the local operation knowledge that contains the contribution ratio of each neighborhood operation is designed to determine the execution times of each neighborhood operation for each generation. Simulation experiments on different instances and comparisons of algorithms show the effectiveness of the proposed algorithm.

Recommended Citation

Rong, Hu; Chen, Wenbo; Qian, Bin; Ning, Guo; and Xiang, Fenghong (2021) "Learning Ant Colony Algorithm for Green Multi-depot Vehicle Routing Problem," Journal of System Simulation: Vol. 33: Iss. 9, Article 11.
DOI: 10.16182/j.issn1004731x.joss.20-0349
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss9/11

First Page

2095

Revised Date

2020-08-07

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0349

Last Page

2108

CLC

TP391.9

Recommended Citation

Hu Rong, Chen Wenbo, Qian Bin, Guo Ning, Xiang Fenghong. Learning Ant Colony Algorithm for Green Multi-depot Vehicle Routing Problem[J]. Journal of System Simulation, 2021, 33(9): 2095-2108.

DOI

10.16182/j.issn1004731x.joss.20-0349