Optimizing Dynamic Flexible Job Shop Scheduling Problem Based on Genetic Algorithm

Abstract

Scheduling the flexible job shop in the dynamic environment, in which arriving new job, the breakdown of machines and processing time variation are possible, is the most complex problem in the manufacturing system until now. A genetic algorithm (GA) was developed to deal with the problem related to flexible job shop scheduling problem represented in routing and sequencing the operations, besides the problem related to dynamic environment represented in appearing new events such as new job arrival and processing time variation. The algorithm incorporated the traditional procedures of GA with a repair strategy in order to optimize the makespan of dynamic flexible job shop scheduling problem (DFJSSP). The results indicate that the proposed algorithm is effective for solving DFJSSP.