Abstract
The majority of the cell formation models consider grouping of parts and machines, based on clustering techniques. The performance of cells thus formed indicates that the cellular systems perform more poorly in terms of work-in-process inventory, average job waiting time and job flow time than the improved job shops. However, they have superior performance in terms of average move times and setup times. The main reason for such a poor performance is that the current cell design procedures do not consider the operational aspects during the cell formation. Therefore, the objective of this paper is to consider the investment and operational costs simultaneously during the design of a cellular manufacturing system. For this purpose we develop a mixed integer programming model and illustrate the trade-off relationships between the investment and operational variables, such as sequence dependence setup, machine idle time, part inventory, part early and late finish (compared with due date), by considering examples. Computational experience is provided for randomly generated test problems.
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