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Particle Swarm Optimization (PSO) has many successful applications on solving continuous optimization problems. It has been adapted to solve discrete optimization problems using different variants, such as integer PSO (IPSO), discrete PSO (DPSO) and integer and categorical PSO (ICPSO). ICPSO, a recent PSO variant, uses probability distributions instead of the solution values. In this study, we applied ICPSO algorithm to solve dynamic integrated process planning, scheduling and due date assignment (DIPPSDDA) problem which is a higher integration level of well-known problems which are integrated process planning and scheduling (IPPS) and scheduling with due date assignment (SWDDA). Briefly, due date assignment function is integrated to IPPS problem as the third manufacturing function in DIPPSDDA. Furthermore, DIPPSDDA performs scheduling function in a dynamic environment in where jobs arrive to shop floor in any time. The objective of DIPPSDDA problem is to minimize the earliness, tardiness and given due dates length. Since the experimental results show that ICPSO does not find better solutions, crossover and mutation operators used in genetic algorithm were implemented to ICPSO, namely modified ICPSO (MICPSO). Finally, experimental results indicate that the proposed MICPSO provides better performance as compared to genetic algorithm, ICPSO and modified discrete PSO.

A modified integer and categorical PSO algorithm for solving integrated process planning, dynamic scheduling and due date assignment problem