Minimization of Makespan and Total Completion Time for Hybrid Job Shop Scheduling Problem Using Genetic Approaches
Abstract
This paper deals with two different versions of Hybrid Job Shop Scheduling Problems (HJSP); the minimization of the maximum completion time (makespan) and the minimization of the total completion time. State of the art shows that the literature on HJSP is rather scarce and that the majority of works concern the general problem called Flexible Job Shop Scheduling Problem (FJSP) in which parallel machines of a stage may have different speeds or yields. We propose the use of a genetic algorithm (GA) and a hybrid version of a GA (HGA) that applies a stochastic local search with two operators, specifically adapted to the HJSP. To conduct a clear statistical study based on the GA, HGA, and other state-of-the-art approaches, we extended our testbed to cover many existing benchmarks. The results of our experimental study show that our proposed algorithms improve the best-known results on a large set of benchmarks found in the literature. The scalability study shows that the proposed algorithm scales better and can deal with larger instances in the literature.
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