The design of a cellular manufacturing system requires that a part population, at least minimally described by its use of process technology (part/machine incidence matrix), be partitioned into part families and that the associated plant equipment be partitioned into machine cells. At the highest level, the objective is to form a set of completely autonomous units such that inter-cell movement of parts is minimized. We present an integer program that is solved using a genetic algorithm (GA) to assist in the design of cellular manufacturing systems. The formulation uses a unique representation scheme for individuals (part/machine partitions) that reduces the size of the cell formation problem and increases the scale of problems that can be solved. This approach offers improved design flexibility by allowing a variety of evaluation functions to be employed and by incorporating design constraints during cell formation. The effectiveness of the GA approach is demonstrated on several problems from the literature.

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This paper presents a new model that integrates design and planning to prescribe a cost-effective cellular configuration that is responsive to real world considerations. The model incorporates practical engineering features such as the finite capacity of machines, use of alternative machines, multiple "copies" of a machine type, and limitations on cell size. It integrates design decisions, locating machines in each cell and identifying product families, with planning considerations, assuring that machine capacities are sufficient to produce required volumes and dealing with between cell movement to use alternative machines. Computational experience using a commercially available optimization package demonstrates that run time required to resolve problems of realistic size and scope can be quite reasonable.

Abstract. This paper addresses the scheduling of robotic cells with two and three machines with fuzzy methodology. Some of the scheduling problems of these categories are transferred into a solvable traveling salesman problem that can be solved in a polynomial time. For generating the optimal part sequencing in the cells, the Gilmore and Gomory algorithm is modified and instead, a fuzzy Gilmore and Gomory algorithm is developed. Then, the proposed algorithm is tested and verified in a supplier company that produces part for an automobile industry. In any case, we compare the results of the proposed fuzzy method with those of crisp ones. The results show the superiority of the proposed algorithm in terms of flexibility, robustness, and reduction of cycle times.

Cellular manufacturing emerged as a production strategy capable of solving the problems of complexity and long manufacturing lead times in batch production. The fundamental problem in cellular manufacturing is the formation of product families and machine cells. This paper presents a new approach for obtaining machine cells and product families. The approach combines a local search heuristic with a genetic algorithm. Computational experience with the algorithm on a set of group technology problems available in the literature is also presented. The approach produced solutions with a grouping efficacy that is at least as good as any results previously reported in literature and improved the grouping efficacy for 59 % of the problems. Keywords: Cellular Manufacturing; Group Technology; Genetic Algorithms; Random Keys AT&T Labs Research Technical Report TD-5FE6RN, October 29, 2002.

Similar product designs resemble each other and have similar attributes and characteristics. When examining a product design to create its process plan, design its fixture, or estimate its manufacturing cost, manufacturing engineers often identify one or more similar products that the factory has manufactured in the past. They may do this from memory, from a file cabinet, or from a database (in a product data management system). Then, they retrieve the process plan (or fixture or cost estimate) for the similar product and modify it for the new product. Manufacturing and process planning experts use a complex set of rules, guidelines, and other knowledge to determine how similar two products are. Computer-aided process planning tools, however, generally use simpler, less sophisticated procedures for determining similarity. These traditional procedures may be inappropriate for specific settings. This paper presents an approach for developing function-specific design similarity measures. Such a measure explicitly exploits the specific need for similar products and thus improves variant approaches for process planning, fixture planning, and manufacturability evaluation. The approach is applied to a specific fixture planning domain.

Abstract—Cell formation is the first step in the design of cellular manufacturing systems. In this study, a general purpose computational scheme employing a hybrid tabu search algorithm as the core is proposed to solve the cell formation problem and its variants. In the proposed scheme, great flexibilities are left to the users. The core solution searching algorithm embedded in the scheme can be easily changed to any other meta-heuristic algorithms, such as the simulated annealing, genetic algorithm, etc., based on the characteristics of the problems to be solved or the preferences the users might have. In addition, several counters are designed to control the timing of conducting intensified solution searching and diversified solution searching strategies interactively. T Keywords—Cell formation problem, Tabu search

Cellular manufacturing emerged as a production strategy capable of solving the problems of complexity and long manufacturing lead times in batch production. The fundamental problem in cellular manufacturing is the formation of product families and machine cells. This paper presents a new approach for obtaining simultaneous arrangement of part families and machine cells for cellular manufacturing systems. The main feature of the proposed method is, the relevant production data such as process sequences and setup times are taken in to account. It has the ability to select the best solution among the solutions of compactness, group technology efficiency and reducing setup time efficiency for each part before attempting to cluster the machines and parts. The formation of part family and machine cell has been treated as a maximization problem according to a defined performance measure ‘β’. A genetic algorithm has been developed for solving the cell formation problem considering the reduction in setup time. The validation has been done based on a real time manufacturing data. This algorithm is written in the ‘C’ language on Intel Pentium / PIII compatible system.

A study on role of cellular design in rate of personnel’s satisfaction and flexibility of production and montage lines: Case-working- clothing industryproduction company