An Approach for Flexible Job-Shop Scheduling with Separable Sequence-Dependent Setup Time

Job-Shop Scheduling Problem Is considered as one of the hardest combinatorial optimization problem. Any known algorithm is not capable to find an optimum solution in polynomial time that characterizes it as NP-complete problem. There are many applications of this problem where considering the setup time is necessary (cost), which is the work to prepare the machines for the execution of jobs. This aspect has been neglected or considered as part of the processing time of the jobs. In fact, this is another difficulty to cope with this problem. Approximate resolution methods are extensively used as an interesting alternative. In this sense the genetic algorithms are suitable techniques. This work presents an innovative and efficient proposal based on genetic algorithms for the Flexible Job-Shop Scheduling Problem, an extension of this problem, which main aspects includes the separable sequence-dependent setup time and multicriteria optimization.