Reinforcement Learning-Based Multiobjective Evolutionary Algorithm for Mixed-Model Multimanned Assembly Line Balancing Under Uncertain Demand

In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimann...

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Bibliographic Details
Published in:IEEE transactions on cybernetics Vol. 54; no. 5; pp. 1 - 14
Main Authors: Zhang, Zikai, Tang, Qiuhua, Chica, Manuel, Li, Zixiang
Format: Journal Article
Language:English
Published: United States IEEE 01.05.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Assembly line balancing (ALB)
Assembly lines
Balancing
Costs
Demand
Evolutionary algorithms
Genetic algorithms
Idling
Integer programming
Job shop scheduling
Linear programming
Machine learning
Mixed integer
multimanned
multiobjective optimization
Multiple objective analysis
Mutation
Operators
Optimization
Production
reinforcement learning (RL)
Robustness
Strategy
Task analysis
uncertain demand
Workstations
ISSN:2168-2267, 2168-2275, 2168-2275
Online Access:Get full text
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Summary:In practical assembly enterprises, customization and rush orders lead to an uncertain demand environment. This situation requires managers and researchers to configure an assembly line that increases production efficiency and robustness. Hence, this work addresses cost-oriented mixed-model multimanned assembly line balancing under uncertain demand, and presents a new robust mixed-integer linear programming model to minimize the production and penalty costs simultaneously. In addition, a reinforcement learning-based multiobjective evolutionary algorithm (MOEA) is designed to tackle the problem. The algorithm includes a priority-based solution representation and a new task-worker-sequence decoding that considers robustness processing and idle time reductions. Five crossover and three mutation operators are proposed. The <inline-formula> <tex-math notation="LaTeX">Q</tex-math> </inline-formula>-learning-based strategy determines the crossover and mutation operator at each iteration to effectively obtain Pareto sets of solutions. Finally, a time-based probability-adaptive strategy is designed to effectively coordinate the crossover and mutation operators. The experimental study, based on 269 benchmark instances, demonstrates that the proposal outperforms 11 competitive MOEAs and a previous single-objective approach to the problem. The managerial insights from the results as well as the limitations of the algorithm are also highlighted.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2022.3229666