Solving a job shop scheduling problem

Abstract

ABSTRACT Job shop scheduling is a highly nondeterministic polynomial combinatorial issue. In this regard, optimization is essential for reducing the time consumed to perform a task. This research paper proposes an evolved social spider optimization method to deal with the job shop scheduling problem. The evolved social spider optimization method was developed by combining both the differential evolutionary and the social spider optimizations. The key proposal is to minimize the makespan time and solve Job shop scheduling problems to improve productivity. The differential evolutionary algorithm is integrated into the spider position update to boost the exploration capabilities of the social spider optimization algorithm. The time taken for ‘‘i’ jobs and ‘‘j’ machines to perform their tasks is studied. Consequently, 23 benchmark problems were prosperously studied utilizing the proposed techniques, and the computational results were compared with previous Meta heuristics methods. An all-inclusive comparison process was carried out to rate the efficiency of the existing optimization techniques in solving job shop scheduling problems. The proposed method of evolved social spider optimization has emerged as the most promising methodology in solving the job shop scheduling problem by consuming minimum makespan time.