Traditionally, process planning and scheduling are two independent essential functions in a job shop manufacturing environment. In this paper, a unified representation model for integrated process planning and scheduling (IPPS) has been developed. Based on this model, a modern evolutionary algorithm, i.e. the particle swarm optimisation (PSO) algorithm has been employed to optimise the IPPS problem. To explore the search space comprehensively, and to avoid being trapped into local optima, the PSO algorithm has been enhanced with new operators to improve its performance and different criteria, such as makespan, total job tardiness and balanced level of machine utilisation, have been used to evaluate the job performance. To improve the flexibility and agility, a re-planning method has been developed to address the conditions of machine breakdown and new order arrival. Case studies have been used to a verify the performance and efficiency of the modified PSO algorithm under different criteria. A comparison has been made between the result of the modified PSO algorithm and those of the genetic algorithm (GA) and the simulated annealing (SA) algorithm respectively, and different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in optimising the IPPS problem.
- simulated annealing
- genetic algorithm
- particle swarm
- integrated process planning and scheduling