Genetic Algorithm Nested with Simulated Annealing for Big Job Shop Scheduling Problems

In so many combinatorial optimization problems, Job shop scheduling problems have earned a reputation for being difficult to solve. Genetic algorithm has demonstrated considerable success in providing efficient solutions to many non-polynomial-hard optimization problems. In the field of job shop scheduling, genetic algorithm has been intensively researched, but it´s converge speed is not favorable. To solve this issue, in this paper, we proposed a novel method that is genetic algorithm nested with local search procedure. After crossing and mutation operations in every generation of genetic algorithm, a local search operation be carried out form every population individual. In our experiments, some big benchmark problems were tried with the proposed algorithm for validation, and the results are encouraging.