A Review of Scheduling Methods for Multi-AGV Material Handling Systems in Mixed-Model Assembly Workshops

Tianyuan Mao

doi:10.54691/p4x5a536

Authors

Tianyuan Mao

DOI:

https://doi.org/10.54691/p4x5a536

Keywords:

Workshop Material Handling System; Multi-AGV Scheduling; Solution Method.

Abstract

Currently, automobile production in workshops faces demands for multi-variety, small-batch, and rapid delivery. As a key auxiliary link, optimizing the performance of the workshop material scheduling system can enhance production efficiency and economic benefits. With the expansion of enterprise scale and the complexity of production requirements, multi-AGV material handling systems have become an effective solution to optimize production processes and save costs due to their parallel collaboration advantages. However, due to the NP-hard nature of this problem, traditional exact algorithms often perform poorly when dealing with complex large-scale workshop scheduling problems. Therefore, this paper explores the applications of intelligent algorithms such as genetic algorithms, artificial neural networks, and particle swarm optimization, and proposes novel and efficient solutions for scheduling methods of multi-AGV material handling systems in mixed-model assembly workshops. In addition, to address the problem of a large state space in workshop material handling system scheduling schemes, this paper also discusses the potential applications of emerging technologies such as reinforcement learning. Through these studies, it aims to optimize workshop production processes, reduce production costs, and promote the development of the manufacturing industry.

Downloads

Download data is not yet available.

References

[1] Jainury, Suhartini Mohd, et al. "Integrated Set Parts Supply system in a mixed-model assembly line." Computers & Industrial Engineering 75 (2014): 266-273.

[2] Kilic S H ,Durmusoglu B M .Advances in assembly line parts feeding policies: a literature review[J].Assembly Automation,2015,35(1):57-68.

[3] Emde S ,Gendreau M .Scheduling in-house transport vehicles to feed parts to automotive assembly lines[J].European Journal of Operational Research,2016,260(1):255-267.

[4] Emde, Simon, and Lukas Polten. "Sequencing assembly lines to facilitate synchronized just-in-time part supply." Journal of Scheduling 22 (2019): 607-621.

[5] Hai M Y ,Dong Y L ,Que F P , et al.Dual-resource integrated scheduling method of AGV and machine in intelligent manufacturing job shop[J].Journal of Central South University,2021,28(8):2423-2435.

[6] 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[J].Computers Industrial Engineering,2015,862-13.

[7] Ham, Andy. "Transfer-robot task scheduling in job shop." International Journal of Production Research 59.3 (2021): 813-823.

[8] Yaqin Zhou, et al. "Research on multi-AGV/RGV scheduling method in intensive storage environment." Journal of Mechanical Engineering 57.10 (2021): 245-256.

[9] Liang, Kaibo, et al. "Research on a Dynamic Task Update Assignment Strategy Based on a “Parts to Picker” Picking System." Mathematics 11.7 (2023): 1684.

[10] Church L K, Uzsoy R. Analysis of periodic and event-driven rescheduling policies in dynamic shops[J]. International Journal of Computer Integrated Manufacturing, 1992, 5(3): 153-163.

[11] Vieira G E, Herrmann J W, Lin E. Rescheduling manufacturing systems: a framework of strategies, policies, and methods[J]. Journal of Scheduling, 2003, 6(1): 39-62.

[12] Tang, Hongtao, et al. "A DQL-NSGA-III algorithm for solving the flexible job shop dynamic scheduling problem." Expert Systems with Applications 237 (2024): 121723.

[13] Liu, Feige, Chao Lu, and **n Li. "A History-Guided Regional Partitioning Evolutionary Optimization for Solving the Flexible Job Shop Problem with Limited Multi-load Automated Guided Vehicles." arxiv preprint arxiv:2409.18742 (2024).

[14] Meng, Leilei, et al. "An improved genetic algorithm for solving the multi-AGV flexible job shop scheduling problem." Sensors 23.8 (2023): 3815.

[15] Zhao, Nan, and Chun Feng. "Research on Multi-AGV Task Allocation in Train Unit Maintenance Workshop." Mathematics 11.16 (2023): 3509.

[16] Wang, Xue, et al. "Effective metaheuristic and rescheduling strategies for the multi-AGV scheduling problem with sudden fa

[17] Zhou, **nxin, et al. "A Green Flexible Job-Shop Scheduling Model for Multiple AGVs Considering Carbon Footprint." Systems 11.8 (2023): 427.

[18] Zhang, Fayong, Rui Li, and Wenyin Gong. "Deep reinforcement learning-based memetic algorithm for energy-aware flexible job shop scheduling with multi-AGV." Computers & Industrial Engineering 189 (2024): 109917.

[19] Yao, Youjie, **nyu Li, and Liang Gao. "A DQN-based memetic algorithm for energy-efficient job shop scheduling problem with integrated limited AGVs." Swarm and Evolutionary Computation 87 (2024): 101544.

[20] Zou, Wenqiang, et al. "An effective population-based iterated greedy algorithm for solving the multi-AGV scheduling problem with unloading safety detection." Information Sciences 657 (2024): 119949.

[21] Du, Li Zhen, et al. "Research on multi-load AGV path planning of weaving workshop based on time priority." Math. Biosci. Eng 16.4 (2019): 2277-2292.

[22] Shan, Hongying, et al. "Research on pull-type multi-AGV system dynamic path optimization based on time window." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235.7 (2021): 1944-1955.

[23] Sudiarso, A., and A. W. Labib. "A fuzzy logic approach to an integrated maintenance/production scheduling algorithm." International Journal of Production Research 40.13 (2002): 3121-3138.

[24] Bilkay, O., O. Anlagan, and S. E. Kilic. "Job shop scheduling using fuzzy logic." The International Journal of Advanced Manufacturing Technology 23 (2004): 606-619.

[25] RAMKUMAR, R., A. TAMILARASI, T. DEVI. A Real Time Practical Approach for Multi Objective Job Shop Scheduling using Fuzzy Logic Approach[J]. Journal of computer sciences,2012,8(4):606-612.

[26] Han, **aoqing, et al. "A dual population collaborative genetic algorithm for solving flexible job shop scheduling problem with AGV." Swarm and Evolutionary Computation 86 (2024): 101538.

[27] Belabid, Jabrane. "Fire and manoeuvrer optimizer for flow shop scheduling problems." Evolutionary Intelligence 17.2 (2024): 977-991.

[28] Hu, Hao, et al. "Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0." Computers & Industrial Engineering 149 (2020): 106749.

[29] Zhao, Yejian, et al. "Dynamic jobshop scheduling algorithm based on deep Q network." Ieee Access 9 (2021): 122995-123011.

[30] Wu, Zufa, et al. "Efficient multi-objective optimization on dynamic flexible job shop scheduling using deep reinforcement learning approach." Processes 11.7 (2023): 2018.

[31] Zhao, Cong, and Na Deng. "An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems." Mathematical Biosciences and Engineering 21.1 (2024): 1445-1471.

[32] Zhang, Yi, et al. "Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems." Robotics and Computer-Integrated Manufacturing 78 (2022): 102412.

[33] Zhang, Jia-Dong, et al. "DeepMAG: Deep reinforcement learning with multi-agent graphs for flexible job shop scheduling." Knowledge-Based Systems 259 (2023): 110083.

[34] Liu, Renke, Rajesh Piplani, and Carlos Toro. "A deep multi-agent reinforcement learning approach to solve dynamic job shop scheduling problem." Computers & Operations Research 159 (2023): 106294.

[35] İnal, Ali Fırat, et al. "A multi-agent reinforcement learning approach to the dynamic job shop scheduling problem." Sustainability 15.10 (2023): 8262.

[36] Jing X , Yao X , Liu M ,et al.Multi-agent reinforcement learning based on graph convolutional network for flexible job shop scheduling[J].Journal of Intelligent Manufacturing, 2024(1):35.

[37] Pu, Yu, Fang Li, and Shahin Rahimifard. "Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments." Sustainability 16.8 (2024): 3234.