Abstract
In this paper, we aim to design cellular manufacturing systems that optimize the performance of a manufacturing system subject to the optimization aspects of production planning. Consequently, the demand for each part – one of the production planning features – plays a vital role in determining the part families and the formation of machine cells in each period. In our study, holding and backorder costs follow a probabilistic structure, and they are described by a set of stochastic scenarios. In this model, five objective functions are employed: one of them minimizes the expected total holding and backorder costs in order to evaluate the risk in the model. The aim of this model is to select and optimize the assignment of parts and machines to different cells as well as the number of each produced part in each period. A new heuristic algorithm based on the optimization method is established to yield the best solution for this complicated mathematical formulation. Further, the performance of the proposed algorithm is verified using certain test problems in which the obtained results are compared with those obtained using the branch-and-bound algorithm and heuristic procedures.
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