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. A mixed integer programming model is developed for the given problem that belongs to NP-hard class. In this case, traditional approaches cannot reach to an optimal solution in a reasonable time. Thus, we propose an efficient multi-objective hybrid genetic algorithm.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 prove the efficiency of our proposed HGA, a number of test problems are solved. Its reliability 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.