Duplicate Genetic Algorithm (DGA) for Scheduling a Bi-Objective Flexible Job Shop Problem

This paper addresses the permutation of flexible job shop problem to minimize the makespan and total idleness criteria as bi-objective problem. The Mentioned optimization problem is NP-hard problem because a large solution space allocated to it. Weʹll use Duplicate genetic algorithm to solve the problem which is called DGA, and also it is developed genetic algorithm procedure. Since the proposed DGA approach is being on the basis of GA algorithm, often does offer better solution than the GA algorithm because it includes the rational and appropriate justification. In the DGA algorithm is used the useful features and concepts of “elitism” and “local search", simultaneously. The DGA algorithm provides a local search for the best solution in every generation with the neighborhood structure in several stages and stores them in an external list for reuse as secondary population of Genetic Algorithm .The algorithm performance is evaluated by numerical experiments. By observing the results of DGA method and comparison the results with other methods, we realize that The proposed approach is efficient and appropriate.