Linear programming embedded particle swarm optimization for solving an extended model of dynamic virtual cellular manufacturing systems

Abstract

The concept of virtual cellular manufacturing system (VCMS) is finding acceptance among researchers as an extension to group technology. In fact, in order to realize benefits of cellular manufacturing system in the functional layout, the VCMS creates provisional groups of resources (machines, parts and workers) in the production planning and control system. This paper develops a mathematical model to design the VCMS under a dynamic environment with a more integrated approach where production planning, system reconfiguration and workforce requirements decisions are incorporated. The advantages of the proposed model are as follows: considering the operations sequence, alternative process plans for part types, machine timecapacity, worker time-capacity, cross-training, lot splitting, maximal cell size, balanced workload for cells and workers. An efficient linear programming embedded particle swarm optimization algorithm is used to solve the proposed model. The algorithm searches over the 0-1 integer variables and for each 0-1 integer solution visited; corresponding values of integer variables are determined by solving a linear programming sub-problem using the simplex algorithm. Numerical examples show that the proposed method is efficient and effective in searching for near optimal solutions.