An improved multi-objective optimization algorithm

Paper title: An improved multi-objective optimization algorithm for flflexible job shop dynamic scheduling problem
Authors names: Hongcheng Wang*, Hao Wang*, Hao Luo*,

Affiliation: *Department of Control Science and Engineering, Harbin Institute of Technology, Harbin 150001, China.

Abstract: For manufacturing industry, scheduling problem is a very important problem. Good scheduling scheme can greatly
improve the production effificiency of enterprises. The flflexible 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 effificiency. Dynamic flflexible 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.