Integration of Process Planning and Scheduling Using Modified Particle Swarm Optimization Algorithm

Abstract

Process planning and scheduling are two of the most important manufacturing functions which are usually performed sequentially in traditional approaches. Considering the fact that these functions are usually complementary, it is necessary to integrate them so as to improve performance of a manufacturing system. This paper conceptualizes a multi-agent methodology by considering four intelligent agents (job, machine, tool, and optimization agent) and presents developed modified particle swarm optimization (mPSO) algorithm to solve this combinatorial optimization problem effectively. In order to improve the search efficiency and increase ability to find global optimum, proposed mPSO algorithm has been enhanced with new crossover and mutation operators. Experimental results show applicability of the proposed approach in solving integrated process planning and scheduling problem.