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

Abstract

Particle Swarm Optimization (PSO) has many successful applications in solving continuous optimization problems. It has been adopted 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 solution values. In this study, ICPSO algorithm is applied to solve the Dynamic Integrated Process Planning, Scheduling, and Due Date Assignment (DIPPSDDA) problem, which is a higher integration level of well-known problems including Integrated Process Planning and Scheduling (IPPS) and Scheduling With Due Date Assignment (SWDDA). Briefly, the due date assignment function is integrated into IPPS problem as the third manufacturing function in DIPPSDDA. Furthermore, DIPPSDDA implements the scheduling function in a dynamic environment where jobs arrive at the shop floor at any time. The objective of the DIPPSDDA problem is to minimize the earliness, tardiness, and length of given due dates. Since experimental results show that ICPSO converges, crossover and mutation operators used in genetic algorithms are applied to ICPSO, namely Modified ICPSO (MICPSO). Finally, experimental results indicate that the proposed MICPSO outperforms genetic algorithms, ICPSO, and modified DPSO.