Multi-objective machine-part cell formation through parallel simulated annealing

Group technology (GT) is a manufacturing philosophy which identifies and exploits the similarity of parts and processes in design and manufacturing. A specific application of GT is cellular manufacturing (CM). The first step in the preliminary stage of cellular manufacturing system (CMS) design is cell formation, generally known as a machine-part cell formation (MPCF) or a machine-component grouping (MCG) problem. Simulated annealing (SA) is not only a highly effective and general random search method to obtain near-global optimal solutions for optimization problems, but also quite appropriate for the MPCF problem which is an NP complete, complex problem. In this study, we introduce modified SA with the merits of a genetic algorithm (GA), called parallel SA (PSA), and propose a PSA-based procedure to solve the MPCF problem. More specifically, this study aims to minimize (1) total cost which includes intercell and intracell part transportation cost and machine investment cost, (2) intracell machine loading unbalance and (3) intercell machine loading unbalance under many realistic considerations. The illustrative example, comparisons and analysis demonstrate the effectiveness of this procedure. The proposed procedure is extremely adaptive, flexible, efficient and can be used to solve real MPCF problems in factories by providing a robust manufacturing cell formation in a short execution time.