Improved vector evaluated genetic algorithm with archive for solving multiobjective PPS problem

The process planning and scheduling (PPS) is to determine a solution (schedule), which tells a production facility what to make, when, and on which equipment, to process a set of parts with operations effectively. Multiobjective PPS problems become more complex because the decision maker need to make a trade-off between two or more objectives while determining a set of optimal nondominated solutions effectively. The previous research works use evolutionary algorithms (EA) to solve such problems, however, the proposed approaches cannot get a good balance between efficacy and efficiency. This paper proposed an improved vector evaluated genetic algorithm with archive (iVEGA-A) mechanism to deal with PPS problem while considering the minimization of the makespan and minimization of the variation of workload of machine. The proposed algorithm has been compared with other approaches to verify and benchmark the optimization reliability on PPS problems. These comparisons indicate iVEGA-A is better than vector evaluated genetic algorithm (VEGA) did on efficacy and negligible difference on efficiency. The efficacy is not less than some famous approaches, such as, nondominated sorting genetic algorithm II (NSGA-II) and strength Pareto evolutionary algorithm 2 (SPEA2) and the efficiency is obviously better than the latter.