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A Q-learning antibody evolution algorithm for multi-objective flexible job shop problem with fuzzy processing time
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AI
DOI：10.1007/s00500-025-10721-w.png)
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Abstract
En 中文
Multi-objective flexible fuzzy job shop scheduling problem(MOFFJSSP) is a combination of scheduling problem and fuzzy system, which simulates the uncertainty in the actual production. Due to the high complexity of MOFFJSSP, it has received widespread attention. In this work, a mixed integer linear programming model of MOFFJSSP is considered and a Q-learning antibody evolution algorithm(QAEA) is proposed to minimize fuzzy makespan and fuzzy total machine load. In this approach, (1) a reinforcement learning-based parameter adaptive adjustment(RLPAD) method is adopted to improve the diversity performance; (2) inspired by artificial immune algorithms(AIA), the antibody neighborhood density(AND) operator is proposed to further maintain population diversity; (3) four problem-specific neighborhood structures are designed to enhance exploitation ability and convergence performance. Finally, to verify the effectiveness of QAEA, it is compared with other state-of-art algorithms on 23 benchmarks, the results demonstrate that QAEA can obtain the best pareto solution set in the most of benchmarks.
Keywords:
Multi-objective optimization
Fuzzy processing time
Flexible job shop scheduling problem
Q-learning
Antibody neighborhood density operator
Journal
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2.5
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1.0W
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2.1W