Research on New Flexible Job Shop Scheduling Problem Using Improved Genetic Algorithm

Field research on a leading automotive mold manufacturer in China shows that the flexible job shop, which consists of clamping area-waiting area-processing area and transportation based on truss manipulator, has been widely used for its high flexibility and stability. The integration of clamping, measuring, machining and transportation in the flexible job shop is lacking in the existing research. Therefore, this paper proposed a method to solve the flexible job shop scheduling problem which based on the clamping area-waiting area-processing area with truss manipulator (CWPFJSP-TM). First, a mixed-integer programming (MIP) model of CWPFJSP-TM is constructed with the objective of minimizing the maximum completion time. Then, an improved genetic algorithm (IGA-GFSSDBO) combining genetic fusion strategy (GFS) and self-learning dung beetle optimization (SDBO) is proposed to solve this complex scheduling problem efficiently. Finally, computational experiments are conducted based on real data from automotive mold manufacturers, and the results verify the effectiveness and superiority of the proposed algorithm for solving CWPFJSP-TM.