Reinforcement Learning-based Alpha-list Iterated Greedy for Production Scheduling
Permanent lenke
https://hdl.handle.net/10037/35275Dato
2024-10-11Type
Journal articleTidsskriftartikkel
Peer reviewed
Sammendrag
Metaheuristics can benefit from analyzing patterns and regularities in data to perform more effective searches in
the solution space. In line with the emerging trend in the optimization literature, this study introduces the
Reinforcement-learning-based Alpha-List Iterated Greedy (RAIG) algorithm to contribute to the advances in
machine learning-based optimization, notably for solving combinatorial problems. RAIG uses an N-List mechanism for solution initialization and its solution improvement procedure is enhanced by Reinforcement Learning
and an Alpha-List mechanism for more effective searches. A classic engineering optimization problem, the
Permutation Flowshop Scheduling Problem (PFSP), is considered for numerical experiments to evaluate RAIG’s
performance. Highly competitive solutions to the classic scheduling problem are identified, with up to 9%
improvement compared to the baseline, when solving large-size instances. Experimental results also show that
the RAIG algorithm performs more robustly than the baseline algorithm. Statistical tests confirm that RAIG is
superior and hence can be introduced as a strong benchmark for future studies.
Forlag
ElsevierSitering
Ying K, Pourhejazy P, Cheng. Reinforcement Learning-based Alpha-list Iterated Greedy for Production Scheduling. Intelligent Systems with Applications. 2024;24Metadata
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Copyright 2024 The Author(s)