Proceedings of the 8th annual conference on Genetic and evolutionary computation
Computers and Industrial Engineering
Computers and Operations Research
A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems
Fundamenta Informaticae - Swarm Intelligence
A meta-heuristic approach to solve a JIT scheduling problem in hybrid flow shop
Engineering Applications of Artificial Intelligence
Co-evolutionary genetic algorithm for fuzzy flexible job shop scheduling
Applied Soft Computing
A Multi-swarm Approach to Multi-objective Flexible Job-shop Scheduling Problems
Fundamenta Informaticae - Swarm Intelligence
Network modeling and evolutionary optimization for scheduling in manufacturing
Journal of Intelligent Manufacturing
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Flexible job-shop scheduling problems are an important extension of the classical job-shop scheduling problems and present additional complexity. Such problems are mainly due to the existence of a considerable amount of overlapping capacities with modern machines. Classical scheduling methods are generally incapable of addressing such capacity overlapping. We propose a multiagent scheduling method with job earliness and tardiness objectives in a flexible job-shop environment. The earliness and tardiness objectives are consistent with the just-in-time production philosophy which has attracted significant attention in both industry and academic community. A new job-routing and sequencing mechanism is proposed. In this mechanism, two kinds of jobs are defined to distinguish jobs with one operation left from jobs with more than one operation left. Different criteria are proposed to route these two kinds of jobs. Job sequencing enables to hold a job that may be completed too early. Two heuristic algorithms for job sequencing are developed to deal with these two kinds of jobs. The computational experiments show that the proposed multiagent scheduling method significantly outperforms the existing scheduling methods in the literature. In addition, the proposed method is quite fast. In fact, the simulation time to find a complete schedule with over 2000 jobs on ten machines is less than 1.5 min.