A Parallel Adaptive Genetic Algorithm for Job Shop Scheduling Problem

In order to enhance the production efficiency, scheduling problem of job-shop has used that thought of complex problem with complicated constraints and structure. This problem is characterized as NP-hard. In most cases, the excessive complexity of the problem makes it difficult to discover the best solution within affordable time. Hence, searching for estimated solutions in polynomial time rather than precise solutions at excessive cost is favored for challenging situations of the problem. In this paper, a parallel genetic algorithm with proposed adaptive genetic operators and migration operation is applied for job-shop scheduling problem. Through tests on numerous different experimental cases, the adaptive operator of genetic algorithm and the parallelism strategy are considerably improving the results effectively while decreasing the computation time. Also, the migration operation gives a greater effect on the performance of the algorithms.