A new hybrid algorithm for solving N jobs and M machines scheduling problems with improved local search techniques

Scheduling is one of the most critical tasks in manufacturing, assembling, service industries, etc. Current research attempts to maximize the makespan of any complex scheduling procedure that involves N jobs and M machines. Conventional algorithms fail to perform global optimum solutions when the number of machines & jobs is high due to the NP-hard nature of the problem. Proposed approach employs a hybrid genetic-simulated annealing algorithm with an improved local searching technique, which converges to the global optimum solutions more quickly. Numerous benchmark problems and case studies from the standard literature have been employed to test the suggested algorithm's performance. Comprehensive computational test results demonstrated that the recommended algorithm outperforms alternative heuristic and traditional approaches.