Authors:
Monique Simplicio Viana
1
;
Orides Morandin Junior
1
and
Rodrigo Colnago Contreras
2
Affiliations:
1
Department of Computing, Federal University of São Carlos, Rod. Washington Luiz KM 235, São Carlos – SP, Brazil
;
2
Department of Computer Sciences, University of São Paulo, Av. Trabalhador São-carlense 400, São Carlos – SP, Brazil
Keyword(s):
Job Shop Scheduling Problem, Genetic Algorithm, Transgenic Operator, Combinatorial Optimization.
Abstract:
In recent years, several research studies have been conducted that use metaheuristics to calculate approximations of solutions for solving NP-Hard problems, within this class of problems there is the Job Shop Scheduling Problem (JSSP), which is discussed in this study. Improved solutions to problems of this type have been created for metaheuristics in the form of additional operators. For the Genetic Algorithm (GA) the transgenic operator has recently been created, whose operation is based on the idea of "genetically modified organisms", with the proposal to direct some population of individuals to a more favorable solution to the problem without removing the diversity of the population with a competitive cost of time. In this study, our main contribution is an adaptation of the GA with transgenic operator to the JSSP. The results obtained by the proposed method were compared with three papers in the literature that work on the same benchmark: one using GA, one using Adaptive GA and
another using Ant Colony Optimization. The results confirm that the GA used with the transgenic operator obtains better results in a competitive processing time in comparison to the other techniques, due to its better targeting in the search space.
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