A novel hybrid algorithm for multi-period production scheduling of jobs in virtual cellular manufacturing systems

Abstract

Virtual cellular manufacturing has attracted a lot of attention in recent years because traditional cellular manufacturing is inadequate under a highly dynamic manufacturing environment. In this paper, a new mathematical model is established for generating optimal production schedules for virtual cellular manufacturing systems operating under a multi-period manufacturing scenario. The objective is to minimize the total manufacturing cost over the entire planning horizon. A hybrid algorithm, based on the techniques of discrete particle swarm optimization and constraint programming is proposed to solve the complex production scheduling problem. Although particle swarm optimization performs competitively with other meta-heuristics for most optimization problems, the evolution process may be stagnated as time goes on if the swarm is going to be in equilibrium, especially for problems with hard constraitns. Constraint programming, on the other hand, is an effective technique for solving problems with hard constraints. However, the technique may be inefficient if the feasible search space is very large. Therefore, the aim of the proposed hybrid algorithm is to combine the complementary advantages of particle swarm optimization and constraint programming to improve its search performance. The effectiveness of the proposed methodology is illustrated by solving a set of randomly generated test problems.