An improved multi-objective optimization algorithm for flexible job shop dynamic scheduling problem

Abstract

For manufacturing industry, scheduling problem is a very important problem. Good scheduling scheme can greatly improve the production efficiency of enterprises. The flexible job shop scheduling problem (FJSP) not only needs to arrange the processing sequence for the operations of each workpiece, but also needs to consider how to allocate machines to the operations to improve the processing efficiency. Dynamic flexible job shop scheduling problem (DFJSP) is based on FJSP, which studies how to dynamically reschedule enterprise production according to the actual situation when disturbance events occur, so as to minimize the impact of emergencies on production. For DFJSP under machine fault, this strategy can dynamically schedule in time for different states before and after the machine fault is repaired, maximize the use of workshop resources, and reduce the impact of machine fault on production. A scheduling scheme combining predictive scheduling and real-time scheduling is proposed for DFJSP under machine fault. Then the standard test cases are used to verify the scheduling scheme from multi-objective with NSGA-II. The experimental results show that the scheduling scheme is feasible.