Advanced Optimization of Flowshop Scheduling with Maintenance, Learning and Deteriorating Effects Leveraging Surrogate Modeling Approaches

Nesrine Touafek (), Fatima Benbouzid-Si Tayeb, Asma Ladj and Riyadh Baghdadi
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Nesrine Touafek: Ecole Nationale Supérieure d’Informatique (ESI), Laboratoire des Méthodes de Conception de Systèmes (LMCS), Oued Smar, Algiers BP 68M-16270, Algeria
Fatima Benbouzid-Si Tayeb: Ecole Nationale Supérieure d’Informatique (ESI), Laboratoire des Méthodes de Conception de Systèmes (LMCS), Oued Smar, Algiers BP 68M-16270, Algeria
Asma Ladj: Railenium Research and Technology Institute, 59540 Valenciennes, France
Riyadh Baghdadi: Division of Science, New York University Abu Dhabi, Abu Dhabi P.O. Box 129188, United Arab Emirates

Mathematics, 2025, vol. 13, issue 15, 1-35

Abstract: Metaheuristics are powerful optimization techniques that are well-suited for addressing complex combinatorial problems across diverse scientific and industrial domains. However, their application to computationally expensive problems remains challenging due to the high cost and significant number of fitness evaluations required during the search process. Surrogate modeling has recently emerged as an effective solution to reduce these computational demands by approximating the true, time-intensive fitness function. While surrogate-assisted metaheuristics have gained attention in recent years, their application to complex scheduling problems such as the Permutation Flowshop Scheduling Problem (PFSP) under learning, deterioration, and maintenance effects remains largely unexplored. To the best of our knowledge, this study is the first to investigate the integration of surrogate modeling within the artificial bee colony (ABC) framework specifically tailored to this problem context. We develop and evaluate two distinct strategies for integrating surrogate modeling into the optimization process, leveraging the ABC algorithm. The first strategy uses a Kriging model to dynamically guide the selection of the most effective search operator at each stage of the employed bee phase. The second strategy introduces three variants, each incorporating a Q-learning-based operator in the selection mechanism and a different evolution control mechanism, where the Kriging model is employed to approximate the fitness of generated offspring. Through extensive computational experiments and performance analysis, using Taillard’s well-known standard benchmarks, we assess solution quality, convergence, and the number of exact fitness evaluations, demonstrating that these approaches achieve competitive results.

Keywords: production scheduling; permutation flowshop scheduling problem; maintenance; artificial bee colony; learning effect; deteriorating effect; surrogate model; kriging (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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