Meta-heuristic approach based on genetic and greedy algorithms to solve flexible job-shop scheduling problem

Job-shop scheduling systems are one of the applications of group technology in industry, the purpose of which is to take advantage of the physical or operational similarities of products in their various aspects of construction and design. Additionally, these systems are identified as cellular manufacturing systems (CMS). In this paper, a meta-heuristic method that is based on combining genetic and greedy algorithms has been used in order to optimize and evaluate the performance criteria of the flexible job-shop scheduling problem. In order to improve the efficiency of the genetic algorithm, the initial population is generated by the greedy algorithm, and several elitist operators are used to improve the solutions. The greedy algorithm that is used to improve the generation of the initial population prioritizes the cells and the job in each cell and, thus, offers quality solutions. The proposed algorithm is tested over the P-FJSP dataset and compared with the state-of-the-art techniques of this literature. To evaluate the performance of the diversity, spacing, quality, and run-time criteria were used in a multi-objective function. The results of the simulation indicate the better performance of the proposed method as compared to the NRGA and NSGA-II methods.