MULTIOBJECTIVE MACHINE-PART CELL-FORMATION THROUGH PARALLEL SIMULATEDANNEALING

Group technology (GT) is a manufacturing philosophy which identifies a nd exploits the similarity of parts and processes in design and manufa cturing. A specific application of GT is cellular manufacturing (CM). The first step in the preliminary stage of cellular manufacturing syst em (CMS) design is cell formation, generally known as a machine-part c ell formation (MPCF) or a machine-component grouping (MCG) problem. Si mulated annealing (SA) is not only a highly effective and general rand om search method to obtain near-global optimal solutions for optimizat ion 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), call parallel SA (PSA ), and propose a PSA-based procedure to solve the MPCF problem. More s pecifically, this study aims to minimize (1) total cost which includes intercell and intracell part transportation cost and machine investme nt cost, (2) intracell machine loading unbalance and (3) intercell mac hine loading unbalance under many realistic considerations. The illust rative example, comparisons and analysis demonstrate the effectiveness of this procedure. The proposed procedure is extremely adaptive, flex ible, efficient and can be used to solve real MPCF problems in factori es by providing a robust manufacturing cell formation in a short execu tion time.