An effective p-median model considering production factors in machine cell/part family formation

This paper is concerned with machine cell/part family (MC/PF) formation in cellular manufacturing. Among the solution methodologies for MC/PF formation, the mathematical programming approach seeks to find the optimal number of machine cells and associated part families. The p-median model has been proven to be a powerful mathematical programming model for solving the MC/PF problem. To cluster machines and parts, a new lrmedian formulation is proposed considering real-world production factors such as the operation sequences and production volumes for parts. Unlike existing p-median models, which rely on the classic binary part-machine incidence matrix (PMIM), this p-median model adopts a new similarity coefficient based on production factors that critically impact the MC/PF formation. A production data based PMIM is used, where each non-binary entry indicates actual intra-cell or inter-cell flows to or from machines by parts. In addition, a new efficiency measure for evaluating the goodness of the solution is proposed. A computational comparison shows the effectiveness of the proposed p-median model in terms of the computation time and solution quality as compared with previous mathematical programming approaches.