Chaotic particle swarm optimization algorithm for flexible process planning

Abstract

A variety of manufacturing operations together with a variety of alternative manufacturing resources provide that most jobs in the modern manufacturing systems may have a large number of alternative process plans. For that reason, obtaining an optimal process plan according to all alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) as well as alternative operations has become a very important task in flexible process planning problem research. In this paper, we present and evaluate a new algorithm for optimization of flexible process plans based on utilization of particle swarm optimization (PSO) algorithm and chaos theory. The main idea is to prevent the convergence of PSO in early stages of optimization process by implementing ten different chaotic maps which enlarge search space and provide its diversity. The flexible process plans are represented by using AND/OR network, and machine flexibility, tool flexibility, tool access direction (TAD) flexibility, process flexibility and sequence flexibility are considered. Further, mathematical models for minimization of production time and total production cost are derived. The newly developed algorithm is extensively experimentally verified by using four experimental studies, which show that the developed method outperforms genetic algorithm (GA), simulated annealing (SA), hybrid GA-SA and generic PSO based approach.