Integrative Cell Formation and Layout Design in Cellular Manufacturing Systems

Document Type : Research Paper


1 Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA

2 Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran


This paper proposes a new integrative view of manufacturing cell formation and both inter-cell and intra-cell layout problems. Cells formation and their popular bi-directional linear layout are determined simultaneously through a Dynamic Programming algorithm (with the objective of minimizing the inter-cell flow cost under a cell size constraint). This Dynamic Programming algorithm is implemented in a Simulated Annealing approach with Genetic operators to reach near optimal solutions. Moreover, within this approach and by using an Ant Colony Optimization technique, we also solve the intra-cell layout problem, i.e., we also determine how to lay out machines within relative cells. In contrast with most of the available approaches in the literature, we consider: (1) An integrated objective function to minimize overall inter-cell and intra-cell flow costs instead of merely minimizing the number of inter-cell movements/costs. (2) The integrative and simultaneous determination of cell formation and their layout instead of using sequential approaches. (3) All three phases of cell formation, intercell and intra-cell layout design problems, which are all important for overall performance of the system, and (4) An easy to code and solve integrated procedure through implementing metaheuristic approaches. Our computational results show that by incorporating intra-cell decisions in cell formation and inter-cell design process through implementing our proposed integrated approach, a manufacturer can largely reduce her total material flow cost. Particularly, our computational tests show good quality solutions in comparison with the most similar available approach in the literature with an average improvement of 24.97% in total flow cost for a set of randomly generated test problems.


Main Subjects

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