Multi-AGVs scheduling and path planning algorithm in automated sorting warehouse

被引:0
作者
Yu N. [1 ,2 ]
Li T. [1 ,2 ]
Wang B. [1 ,2 ]
Yuan S. [1 ,2 ]
机构
[1] Donlinks School of Economics and Management, University of Science and Technology Beijing, Beijing
[2] Engineering Research Center of MES Technology for Iron & Steel Production, Ministry of Education, Beijing
来源
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2020年 / 26卷 / 01期
基金
中国国家自然科学基金;
关键词
Automated guided vehicle; Automated sorting warehouse; Differential evolution algorithm; Path planning; Scheduling;
D O I
10.13196/j.cims.2020.01.018
中图分类号
学科分类号
摘要
The automated sorting warehouses are operated by multiple AGVs simultaneously, which can quickly sort a large number of packages. How to determine the handling package sequence for AGVs and plan the conflict-free path is the key to the sorting operation. In order to improve sorting efficiency, aiming at minimizing the maximum handling completion time, firstly, the priority of the conflicting AGVs is defined and a path planning algorithm for generating a conflict-free path is proposed. Furthermore, considering the AGV scheduling and path planning comprehensively, an improved differential evolution algorithm is proposed, the algorithm generates the initial population based on the opposition-based learning, uses adaptive mutation and crossover probabilities to evolve individuals, a dynamic differential evolution strategy is designed to improve the convergence speed, the exchange neighborhood and the insertion neighborhood based on the key AGV are designed for local search. The effectiveness of the algorithm is verified by data experiments, and the key parameters are analyzed. © 2020, Editorial Department of CIMS. All right reserved.
引用
收藏
页码:171 / 180
页数:9
相关论文
共 19 条
  • [1] Qiu L., Hsu W.J., Huang S.Y., Et al., Scheduling and routing algorithms for AGVs: A survey, International Journal of Production Research, 40, 3, pp. 745-760, (2002)
  • [2] Caumond A., Lacomme P., Moukrimb A., Et al., An MILP for scheduling problems in an FMS with one vehicle, European Journal of Operational Research, 199, 3, pp. 706-722, (2009)
  • [3] Chawlal V.K., Chanda A.K., Et al., Simultaneous dispatching and scheduling of multi-load AGVs in FMS-A simulation study, Materials Today: Proceedings, 5, 11, pp. 25358-25367, (2018)
  • [4] Mousavi M., Yap H.J., Musa S.N., Et al., Multi-objective AGV scheduling in an FMS using a hybrid of genetic algorithm and particle swarm optimization, Plos One, 12, 3, (2017)
  • [5] Xu L., Chen Y., Gao X., Et al., AGV scheduling optimization in mixed flexible manufacturing cell, Journal of Tongji University: Natural Science, 45, 12, pp. 1839-1846, (2017)
  • [6] Liu E., Yao X., Tao T., Et al., Improved flower pollinaton algorithm for job shop scheduling problems integrated with AGVs, Computer Integrated Manufacturing Systems, pp. 1-38
  • [7] Saidi-Mehrabad M., Dehnavi-Arani S., Evazabadian F., Et al., An Ant Colony Algorithm(ACA)for Solving the New Integrated Model of Job Shop Scheduling and Conflict-Free Routing of AGVs, Computers & Industrial Engineering, 86, pp. 2-13, (2015)
  • [8] Liu E., Yao X., Lan H., Et al., AGV dynamic path planning based on improved genetic algorithm and its implementation, Computer Integrated Manufacturing Systems, 24, 6, pp. 1455-1467, (2018)
  • [9] Baeza D., Ihle C.F., Ortiz, J M., A comparison between ACO and Dijkstra algorithms for optimal ore concentrate pipeline routing, Journal of Cleaner Production, 144, pp. 149-160, (2017)
  • [10] Duchon F., Babinec A., Kajan M., Et al., Path planning with modified a star algorithm for a mobile robot, Procedia Engineering, 96, pp. 59-69, (2014)