A New Multi-objective Job Shop Scheduling with Setup Times Using a Hybrid Genetic Algorithm

This paper presents a new multi-objective job shop scheduling with sequence-dependent setup times. The objectives are to minimize the makespan and sum of the earliness and tardiness of jobs in a time window. Scince a job shop scheduling problem has been proved to be NP-hard in a strong, traditional approaches cannot reach to an optimal solution in a reasonable time. Thus, we propose an efficient multi-objective hybrid genetic algorithm (GA). We assign fitness based dominance relation and weighted aggregate in the genetic algorithm and local search, respectively. We take a variable neighborhood search algorithm as a local improving procedure in the proposed algorithm to the best individuals in the population of GA every specific number generations. To validate the efficiency of our proposed HGA, a number of test problems are solved. Its performance based on some comparison metrics is compared with a prominent multi-objective evolutionary algorithm, namely SPEA-II. The computational results show that the proposed HGA outperforms the SPEAII algorithm.