Multiobjective optimization for complex flexible job-shop scheduling problems

•A flexible job-shop scheduling problem with complex constraints is studied.•Multiple industrially relevant criteria are optimized using Simulated Annealing.•A lexicographic order and weights are both used to model users’ preferences.•A novel criterion modeling the satisfaction of production targets...

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Bibliographic Details
Published in:European journal of operational research 2022-01, Vol.296 (1), p.87-100
Main Authors: Tamssaouet, Karim, Dauzère-Pérès, Stéphane, Knopp, Sebastian, Bitar, Abdoul, Yugma, Claude
Format: Article
Language:English
Subjects:
Computer Science
Flexible job-shop scheduling with batching
Metaheuristics
Multiobjective optimization
Operations Research
OR in semiconductor manufacturing
Scheduling
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Summary:•A flexible job-shop scheduling problem with complex constraints is studied.•Multiple industrially relevant criteria are optimized using Simulated Annealing.•A lexicographic order and weights are both used to model users’ preferences.•A novel criterion modeling the satisfaction of production targets is introduced.•Numerical results on large industrial instances are discussed. In this paper, we are concerned with the resolution of a multiobjective complex job-shop scheduling problem stemming from semiconductor manufacturing. To produce feasible and industrially meaningful schedules, this paper extends the recently proposed batch-oblivious approach by considering unavailability periods and minimum time lags and by simultaneously optimizing multiple criteria that are relevant in the industrial context. A novel criterion on the satisfaction of production targets decided at a higher level is also proposed. Because the solution approach must be embedded in a real-time application, decision makers must express their preferences before the optimization phase. In addition, a preference model is introduced where trade-off is only allowed between some criteria. Two a priori multiobjective extensions of Simulated Annealing are proposed, which differ in how the simultaneous use of a lexicographic order and weights is handled when evaluating the fitness. A known a posteriori approach of the literature is used as a benchmark. All the metaheuristics are embedded in a Greedy Randomized Adaptive Search Procedure. The different versions of the archived GRASP approach are compared using large industrial instances. The numerical results show that the proposed approach provides good solutions regarding the preferences. Finally, the comparison of the optimized schedules with the actual factory schedules shows the significant improvements that our approach can bring.
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2021.03.069