Multi-AGV path planning with double-path constraints by using an improved genetic algorithm

• Published in: PloS one Vol. 12; no. 7; p. e0181747
• Main Authors: Han, Zengliang, Wang, Dongqing, Liu, Feng, Zhao, Zhiyong
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 26.07.2017; Public Library of Science (PLoS)
• Subjects: Algorithms; Automated guided vehicles; Automatic guided vehicles; Automation; Biology and Life Sciences; Chromosomes; Computational Biology - methods; Computer engineering; Computer Simulation; Exchanging; Genetic algorithms; Innovations; Materials handling; Models, Theoretical; Operators; Path planning; Physical Sciences; Reproducibility of Results; Research and Analysis Methods; Simulation; Software; Traveling salesman problem; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0181747

This paper investigates an improved genetic algorithm on multiple automated guided vehicle (multi-AGV) path planning. The innovations embody in two aspects. First, three-exchange crossover heuristic operators are used to produce more optimal offsprings for getting more information than with the traditional two-exchange crossover heuristic operators in the improved genetic algorithm. Second, double-path constraints of both minimizing the total path distance of all AGVs and minimizing single path distances of each AGV are exerted, gaining the optimal shortest total path distance. The simulation results show that the total path distance of all AGVs and the longest single AGV path distance are shortened by using the improved genetic algorithm.