Scheduling and path planning of multiple automatic guided vehicles in container terminals

被引：0
|
作者：
Li J. [1 ]
Zhu X. [1 ,2 ]
机构：
[1] College of Arts and Sciences, Shanghai Maritime University, Shanghai
[2] Institute of Logistics and Engineering, Shanghai Maritime University, Shanghai
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2022年 / 28卷 / 05期
关键词：
Automated container terminal; Conflict prevention; Floyd algorithm; Grey wolf optimizer algorithm; Multiple automatic guided vehicle scheduling; Path planning;
D O I：
10.13196/j.cims.2022.05.016
中图分类号：
学科分类号：
摘要：
Aiming at the scheduling and path planning of multiple Automated Guided Vechicles (AGVs) on automated container terminals, a mathematical model with the goal of minimizing AGV energy consumption was established by considering AGV load and conflicts. Two stages algorithm was designed to solve this model. In the first stage, Grey Wolf Optimizer (GWO) was used to optimize AGV scheduling under the shortest path based on task combination decomposition. In the second stage, for the better AGV scheduling, Floyd based time conflict prediction algorithm was further used to optimize its path to achieve the result of conflict prevention. Experiments verified the feasibility and effectiveness of the designed algorithm under different problem scales. The results showed that the proposed algorithm could effectively reduce the code length, and the result quality, running time and convergence were better than those obtained by other algorithms. It could effectively solve the scheduling of multiple AGVs under different scales and prevent conflict path planning, and reduce the energy consumption of AGVs. © 2022, Editorial Department of CIMS. All right reserved.
引用
收藏
页码：1449 / 1461
页数：12
相关论文
共 29 条
  • [11] ZOU W Q, PAN Q K, TASGETIREN M F., An effective discrete artificial bee colony algorithm for scheduling an automatic-guided-vehicle in a linear manufacturing workshop, IEEE Access, 8, pp. 35063-35076, (2020)
  • [12] NISHI T, MAENO R., Petri net decomposition approach to optimization of route planning problems for AGV systems, IEEE Transactions on Automation Science and Engineering, 7, 3, pp. 523-537, (2010)
  • [13] SHI Jianfeng, YANG Yongsheng, Research on the AGV path planning based on improved Dijkstra algorithm, Science & Technology Vision, 20, pp. 111-112, (2016)
  • [14] YANG Yongsheng, CUI Jiayu, LIANG Chengji, Et al., Research on the routing of AGV in automated container terminal based on soft time window, Journal of Guangxi University: Natural Science, 42, 5, pp. 1793-1801, (2017)
  • [15] ZHANG Z, GUO Q, CHEN J, Et al., Collision-free route planning for multiple AGVs in an automated warehouse based on collision classification, IEEE Access, 6, pp. 26022-26035, (2018)
  • [16] TAI R C, WANG J C, CHEN W D., A prioritized planning algorithm of trajectory coordination based on time windows for multiple AGVs with delay disturbance, Assembly Automation, 39, 5, pp. 753-768, (2019)
  • [17] XU Y X, QI L, LUAN W J, Et al., Load-in-load-out AGV route planning in automatic container terminal, IEEE Access, 8, pp. 157081-157088, (2020)
  • [18] YUAN Z H, YANG Z M, LYU L L, Et al., A bi-level path planning algorithm for multi-AGV routing problem, Electronics, 9, 9, (2020)
  • [19] UMAR U A, ARIFFIN M K A, ISMAIL N, Et al., Hybrid multiobjective genetic algorithms for integrated dynamic scheduling and routing of jobs and automated-guided vehicle (AGV) in flexible manufacturing systems (FMS) environment, The International Journal of Advanced Manufacturing Technology, 81, 9-12, pp. 2123-2141, (2015)
  • [20] ZAGHDOUD R, MESGHOUNI K, DUTILLEUL S C, Et al., A hybrid method for assigning containers to AGVs in container terminal, IFAC-PapersOnLine, 49, 3, pp. 96-103, (2016)
123