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.
The single row facility layout problem (SRFLP) is an important combinatorial optimization problem where a given set of facilities have to be arranged in a single row to minimize the weighted sum of the distances between all pairs of facil-ities. In this paper, ahybrid method for single row facility layout problem is proposed in which, the simulated annealing (SA) is embedded in the clonal selection algorithm (CSA). The performance of the proposed algorithm is tested on benchmark problems. Computational results show the efficiency of the proposed algorithm compared to other heuristics
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