Manufacturing Technology Today

S. B. Barve ¹, P. M. Khodke ²

Affiliations
1 Dept. of Mechanical Engg., Bharati Vidyapeeth's College of Engg., C. B. D., Navi-Mumbai, IN
2 Govt. College of Engg., Amaravati, Maharashtra, IN

Abstract

Cellular Manufacturing System (CMS) provides great benefits as compared to traditional manufacturing systems. The design of cellular manufacturing systems is complex for real life problems. Existing design methods employ simplifying assumptions, which often deteriorate the validity of the models used for obtaining solutions. Assumption used in existing design methods is that product-mix and demand do not change over the planning horizon. The purpose of this study is to develop solution approach for designing cellular manufacturing systems for dynamic production requirements. The CMS model with dynamic production belongs to a class of NP-Complete problems that cannot be solved by traditional optimization methods. Thus, simulated annealing (SA) method is proposed to solve the problem. After discussing the method and presenting the model, it is applied on randomly generated problem based upon published data in the CMS design literature with two period planning horizons to validate the efficacy of the algorithm so developed. The proposed model reduces total cost of CMS.

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S. B. Barve ¹, P. M. Khodke ²

Affiliations
1 Dept. of Mechanical Engg., Bharati Vidyapeeth College of Engg. C.B.D., Navi-Mumbai, IN
2 Government College of Engg., Amaravati, IN

Abstract

Cellular Manufacturing System (CMS) Is a viable option In many manufacturing systems. Success of CMS depends on cell formation method along with Production Control (PC) activities. CMS has drawn the attention of many researchers and practitioners since its inception. But unfortunately, CMS has been addressed as a problem of grouping of machines and components only, with the little attention towards the problems associated with successful implementation of CMS. This paper attempts to define some important production control problems and identifies various factors and features, which influence effective control of design of CMS.

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