A novel hybrid multi-objective algorithm to solve the generalized cubic cell formation problem

The Generalized Cubic Cell Formation Problem (GCCFP) is a Multi-Objective (MO) optimization problem in manufacturing systems. It aims to find the suitable grouping of machines, products, and workers and their best assignment to manufacturing cells. During the resolution of the MO optimization proble...

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Veröffentlicht in:Computers & operations research Jg. 150; S. 106069
Hauptverfasser: Bouaziz, Hamida, Bardou, Dalal, Berghida, Meryem, Chouali, Samir, Lemouari, Ali
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.02.2023
Schlagworte:
AUGMECON-R
Cell formation
Cellular manufacturing
Multi-objective optimization
Simulated Annealing
Cell formation
Cellular manufacturing
AUGMECON-R
Multi-objective optimization
Simulated Annealing
ISSN:0305-0548, 1873-765X
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Zusammenfassung:The Generalized Cubic Cell Formation Problem (GCCFP) is a Multi-Objective (MO) optimization problem in manufacturing systems. It aims to find the suitable grouping of machines, products, and workers and their best assignment to manufacturing cells. During the resolution of the MO optimization problems, the decision maker should take decisions about multiple variables in order to optimize several objectives, or to find a good trade-off between the concerned objectives. Depending on the moment when the decision about the suitable configuration is taken, the resolution methods may be devised into (i) priori methods, (ii) interactive methods, and (iii) posteriori methods. In this study, a novel posteriori efficient algorithm called Robust Simulated Annealing-AUGMented ɛ-CONstraint (SA-AUGMECON-R) algorithm is introduced in order to solve the GCCFP. The proposed algorithm combines AUGMECON-R exact method and the simulated annealing meta-heuristic. To evaluate the performance of the algorithm, a comparative study was conducted. Regarding the values of the metrics that are used to evaluate the performance of MO algorithms, SA-AUGMECON-R algorithm provides excellent results comparing with AUGMECON 2, AUGMECON-R and NSGA-II. •The study targets the Generalized Cubic Cell formation problem (GCCFP).•A novel posteriori efficient algorithm is introduced in order to solve GCCFP.•The algorithm is called Robust Simulated Annealing-AUGMented ϵ-CONstraint algorithm.•SA-AUGMECON-R is a combination of AUGMECON-R exact method and Simulated Annealing.•The algorithm provides excellent results on the used metrics.
ISSN:0305-0548
1873-765X
DOI:10.1016/j.cor.2022.106069