Compound Rules and Reinforcement Learning Based Scheduling Method for Mixed Model Assembly Lines

被引：0

作者：

Guo J. ^{[1
,2
]}

Lyu Y. ^{[3
]}

Dai Z. ^{[1
]}

Zhang J. ^{[3
]}

Guo Y. ^{[2
]}

机构：

[1] Shanghai Spaceflight Precision Machinery Institute, Shanghai

[2] School of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing

[3] Institute of Artificial Intelligence, Donghua University, Shanghai

来源：

Zhongguo Jixie Gongcheng/China Mechanical Engineering | 2023年 / 34卷 / 21期

关键词：

balancing and sequencing; compound rule; deep reinforcement learning; integrated optimization; mixed model assembly line;

D O I：

10.3969/j.issn.1004-132X.2023.21.009

中图分类号：

学科分类号：

摘要：

A scheduling method was proposed based on compound rules and reinforcement learning for balancing and sequencing problems of mixed model assembly lines. A balancing rule set and a sequencing rule set were designed with the consideration of mathematical model, and a proximal policy optimization（P P O ） algorithm featured with Actor-Critic training procedure and preferential experience learning mechanism was employed to regulate weighted parameters of these rules, in order to generate reasonable balancing and sequencing solutions. In comparative experiments, the proposed scheduling method demonstrates the effectiveness over other methods including P P O algorithm with single rule, compound rules, and a genetic algorithm. © 2023 China Mechanical Engineering Magazine Office. All rights reserved.

引用

页码：2600 / 2606and2614

共 18 条

[1] XIAO Jing, Boeing Depicts the Sustainable Development Future of the Aerospace Industry [J], China Civil Aviation, 2021, 10, pp. 58-59
[2] BOCK S, BOYSEN N., Integrated Real-time Control of Mixed-model Assembly Lines and Their Part Feeding Processes[J], Computers & Operations Research, 132, (2021)
[3] LI Yingli, LI Xinyu, Review on H y brid Flow Shop Scheduling Problems[J], China M e chanical Engineering, 31, 23, pp. 2798-2813, (2020)
[4] GIORGIO A, MAFFEI A, ONORI M, Et al., TowardsOnlineReinforcedLearningofAssemblySequence Planning with Interactive Guidance Systems forIndustry4.0 Adaptive Manufacturing[J], Journal of Manufacturing Systems, 60, pp. 22-34, (2021)
[5] RAUF M, GUAN Z, SARFRAZ S, Et al., A Smart Algorithm for Multi-criteria Optimization of Model SequencingProblemin AssemblyLines[J], Roboticsa n d Computer-Integrated Manufacturing, 61, (2020)
[6] AKYOL S D, BAYKASOGLU A., A Multiple-rule Based Constructive Randomized Search Algorithm for Solving Assembly Line Worker Assignment and BalancingProblem[J], JournalofIntelligent Manufacturing, 30, 2, pp. 557-573, (2019)
[7] DEFERSHA F M, MOHEBALIZADEHGASHTI F., SimultaneousBalancing, Sequencing, and WorkstationPlanningfora Mixed ModelManualAssemblyLineUsingHybridGenetic Algorithm[J], Computers & Industrial Engineering, 119, pp. 370-387, (2018)
[8] ZHANG B, XU L, ZHANG J., A Mult-objective CellularGeneticAlgorithmforEnergy-orientedBalancingandSequencingProblemofMixed-modelAssembly Line[J], Journal of Cleaner Production, 244, (2019)
[9] Peng Z H A N G, Related Parallel Machine Online Scheduling Method Based on LSTM-PPO Reinforcement Learning[J], ChinaMechanicalEngineering, 33, 3, pp. 329-338, (2022)
[10] WANG H, SARKERB R, LI J, Et al., Adaptive Scheduling for Assembly Job Shop with Uncertain Assembly Times Based on Dual Q-learning[J], InternationalJournalo f ProductionResearch, 59, 19, pp. 5867-5883, (2021)

← 1 2 →