One of the practical application in cellular manufacturing systems is the cell formation problem (CFP). Its main idea is to group machines into cells and parts into part families in a way that the number of exceptional elements and the number of voids are minimized. In literature, it is proved that p-median is an efficient mathematical programming model for solving CF problems. In the present work, we develop a modified p-median based model dedicated to solve CFP respecting the objective of minimizing the sum of dissimilarities of machines. For this aim, we applied a General Variable Neighborhood Search algorithm and we collaborated it with an Estimation of Distribution Algorithm maximizing the group capability index and the grouping efficacy evaluation criteria. Thirty CF problems are taken from the literature and tested by our proposed algorithm and the experimental study demonstrated that the proposed method guided by p-median model provides high quality cells in speed running times and beats other state-of-the-art algorithms particularly for CF instances with large sizes.

细胞制造系统中的实际应用之一是细胞形成问题（CFP）。它的主要思想是将机器分组为单元，将零件分组为零件族，以最大程度地减少特殊元素的数量和空隙的数量。在文献中，已证明p中值是解决CF问题的有效数学编程模型。在当前的工作中，我们开发了一种基于修正的基于p中值的模型，该模型致力于解决CFP方面的问题，其目标是最大程度地减少机器差异的总和。为此，我们应用了通用变量邻域搜索算法，并将其与最大化组能力指标和分组效能评估标准的分布估计算法配合使用。