In this study, the machining center with the Automated Pallet Changer (APC) scheduling problem considering the disturbance of the first piece inspection is presented. The APC is frequently used in industry practice; it is useful in terms of sustainability and robustness because it increases the machine utilization rate and enhances the responsiveness to uncertainties in dynamic environments. An enhanced evolutionary algorithm for APC scheduling (APCEA) is developed by combining the multi-objective evolutionary algorithm with APC simulation. The dynamic factors in the simulation model include the pass rate of the first piece inspection (FPI) and the adjusted time when the FPI is unpassed. The proposed APCEA defines the non-robust gene based on the risk combination of the first piece inspection, and screens the non-robust gene in the genetic operation, thus improving the solution quality under the same computation times. Compared with the other three multi-objective evolutionary algorithms (MOEAs), it is demonstrated that the proposed APCEA produces the best result among the four methods. The proposed APCEA has been embedded into the manufacturing execution system (MES) and successfully applied in a manufacturing plant. The application value of the proposed method is verified by a practical example.

Based on the non-permutation property of intercell scheduling in flowline manufacturing cells, a hybrid harmony search algorithm is proposed to solve the problem with the makespan criterion. On the basis of the basic harmony search algorithm, the three key elements of memory consideration, pitch adjustment and random selection are discretized and improved to adapt to the operation-based encoding. To compare the performance, different scale cases are generated in both the overall solution and the two-stage solution with the proposed algorithm, the hybrid particle swarm optimization algorithm and the hybrid genetic algorithm. The relative deviation is taken as the performance index. The compared results show that a better solution can be obtained with the proposed algorithm in both the overall solution and the two-stage solution, verifying the superior performance of the proposed algorithm.