Abstract

Recent works are using meta-heuristics to address the problem class known in the literature as Job Shop Scheduling Problem (JSSP) because of its complexity, since it consists of combinatorial problems and belongs to the set of NP-Hard computational problems. In this type of problem, one of the most discussed goals is to minimize the makespan, which is the maximum production time of a series of jobs. A widely used meta-heuristic in JSSP is the Genetic Algorithm (GA) due to its good performance in scheduling problems. However, for problems with high complexity, some form of hybridization in GA may be required to improve search space performance, for example, by including specialized local search techniques. It is proposed in this work the use of specialized and improved local search operators in the meta-heuristic GA with multi-crossover strategies in order to minimize makespan in JSSP: The Multi-Crossover Local Search Genetic Algorithm (mXLSGA). Specifically, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. The developed method has been evaluated on 58 instances from well-established benchmarks. Experimental results have proven that mXLSGA is competitive and versatile compared to the state-of-the-art methods.

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