Multidisciplinary Machine Cell Formation for Design of Cellular Manufacturing Systems

Author(s):  
David He ◽  
Angela Adamyan

Abstract Machine cell formation in design of cellular manufacturing systems has traditionally ignored the issues of reliability. As such, the machine cell formation methods have been developed without the explicit considerations of system reliability and maintainability and the systems designed by these methods may have poor reliability and maintainability and hence result in low availability and productivity. In this paper, we discussed how the reliability issues should be incorporated into solving machine cell formation problems. A new formulation for machine cell formation with reliability considerations in cellular manufacturing systems was developed. The formulation is based on multi-attribute utility theory and represents the tradeoff the designers are willing to make between reliability and other design attributes in design of cellular manufacturing systems. An example was used to illustrate the application of the formulation for machine cell formation with reliability considerations. An algorithm for determining the optimal machine cell size in design of cellular manufacturing systems is also proposed.

Author(s):  
Amin Rezaeipanah ◽  
Musa Mojarad

This paper presents a new, bi-criteria mixed-integer programming model for scheduling cells and pieces within each cell in a manufacturing cellular system. The objective of this model is to minimize the makespan and inter-cell movements simultaneously, while considering sequence-dependent cell setup times. In the CMS design and planning, three main steps must be considered, namely cell formation (i.e., piece families and machine grouping), inter and intra-cell layouts, and scheduling issue. Due to the fact that the Cellular Manufacturing Systems (CMS) problem is NP-Hard, a Genetic Algorithm (GA) as an efficient meta-heuristic method is proposed to solve such a hard problem. Finally, a number of test problems are solved to show the efficiency of the proposed GA and the related computational results are compared with the results obtained by the use of an optimization tool.


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