Job shop scheduling problem using hybrid ant colony optimization and genetic algorithm

Abstract

Job-shop scheduling techniques play a significant role in various parallel applications. An efficient job-shop scheduling technique not only provides high availability of resources to users, but also enhances the performance of parallel machines. Job-shop scheduling techniques are a typical NP-hard problem. Currently, numerous researchers have solved job-shop scheduling problems by considering the well-known metaheuristic techniques. These techniques are Genetic algorithm (GA), Particle swarm optimization (PSO), Variable neighborhood search (VNS), Ant colony optimization (ACO), BAT algorithm (BA), Artificial bee colony (ABC) etc. However, these techniques suffer from one of these issues: premature convergence, poor convergence speed, initially selected random solutions and stuck in local optima. To handle the issues associated with existing metaheuristics based job-shop scheduling techniques, in this research work, a hybrid scheduling techniques is designed. Hybridization of metaheuristic based job-shop scheduling techniques is achieved by integrating the ACO with GA. It has an ability to overcome several issues associated with existing techniques such as premature convergence, poor convergence speed, initially selected random solutions and stuck in local optima issues.