TY - JOUR
T1 - Multi-AGV path planning with double-path constraints by using an improved genetic algorithm
AU - Han, Zengliang
AU - Wang, Dongqing
AU - Liu, Feng
AU - Zhao, Zhiyong
N1 - Publisher Copyright:
© 2017 Han et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/7
Y1 - 2017/7
N2 - 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.
AB - 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.
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U2 - 10.1371/journal.pone.0181747
DO - 10.1371/journal.pone.0181747
M3 - Article
C2 - 28746355
AN - SCOPUS:85026267589
VL - 12
JO - PLoS ONE
JF - PLoS ONE
IS - 7
M1 - e0181747
ER -