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Simulation-based Optimization using Genetic Algorithms for Multi-objective Flexible JSSP

Elena Simona NICOARĂ1, Florin Gheorghe FILIP2,3, Nicolae PARASCHIV1
1 Petroleum-Gas University,
39, Bucureşti Blvd., Ploieşti, 100520, Romania,
snicoara@upg-ploiesti.ro

2 Romanian Academy – INCE and BAR,
125 , Calea Victoriei, Bucharest, 010071, Romania
filipf@acad.ro
3 I C I Bucharest
(National Institute for R & D in Informatics)

8-10 Averescu Blvd.
011455 Bucharest 1, Romania

Abstract: The fast technological progress, along with growing requirements in the manufacturing systems have led in the last decades to a true revolution regarding the optimization methods for job shop scheduling problem (JSSP), which regularly has the greatest impact on the global optimality from the temporal perspective. An extension to the mathematical framework associated to the JSSP for multi-objective flexible JSSP (MOFJSSP) is proposed; here, the flexibility of type II, where the routings of the jobs on the resources are not fixed is considered. Also, a short review of the most used simulation-based optimization methods for (MOF)JSSP is made and a genetic algorithm-based control system is proposed. This is then tested on a complex real-world MOFJSS instance and the ft10 test-instance.

Keywords: Multi-objective Flexible Job Shop Scheduling Problem, Simulation-based Optimization, Genetic Algorithm, GA-based Control, NSGA-II.

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CITE THIS PAPER AS:
Elena Simona NICOARĂ, Florin Gheorghe FILIP, Nicolae PARASCHIV, Simulation-based Optimization using Genetic Algorithms for Multi-objective Flexible JSSP, Studies in Informatics and Control, ISSN 1220-1766, vol. 20 (4), pp. 333-344, 2011.