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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Genetic algorithms(GA) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region at which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, usually working as evaluation functions, and genetic algorithms. There are two basic strategies in using hybrid GAs, Lamarckian and Baldwinian learning. Traditional schema theory does not support Lamarckian learning, i.e., forcing the genetic representation to match the solution found by the improvement procedure. However, Lamarckian learning does alleviate the problem of multiple genotypes mapping to the same phenotype. Baldwinian learning uses improvement procedures to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper empirically examines the issues of using Lamarckian and Baldwinian learning in hybrid GAs. In the empirical investigation cond...

This paper addresses the problem of grouping machines in order to design cellular

This paper presents a new evolutionary approach to the machine-part cell formation (MPCF) problem, generally considered in manufacturing cell design, where a zero-one machine-part matrix must have its rows and columns moved to form machines and parts clusters. The Constructive Genetic Algorithm (CGA) was proposed recently to solve clustering problems, and is applied here to the MPCF. The MPCF is modeled as a bi-objective problem that guides the construction of feasible assignments of machines and parts to specify clusters, and provides evaluation of schemata and structures in a common basis. A particularly derived structure and schema representation considers Jaccard distances for binary strings. A variable size population is formed only by schemata, considered as building blocks for feasible solutions construction along the generations. Recombination gives population diversification, and local search mutation is applied to structures that represent feasible solutions. Experimental res...

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.

heuristic to minimize makespan of cell scheduling problem

This paper gives descriptions on various methods of solving the layout problem and describes a novel method based on genetic algorithms (GA) to solve the machine layout problem. Developing a machine layout is an important step in designing manufacturing facilities due to the impact of the layout to material handling cost and time, and consequently, affects the overall productivity of the shop floor. Poor layout would result in having more parts spending longer time moving from one machine to another, and thus results in increasing material handling costs. In contrast to the block layout, the objective of the machine layout problem is to find the appropriate placement of machines in a cell. The GA-based method developed to solve this uses the objective of minimizing the movements of parts being processed in the cell.

In this paper an agglomerative heuristic cluster analysis framework is proposed for application to the part family and machine cell formation problems associated with Group Technology (GT). This framework addresses the notion of concurrently forming clusters of parts (families) and machines (cells) based upon natural between-part and between-machine relationships and the strength of association relating pairs of parts with pairs of machines. An illustrative model is presented in the conference session and operational aspects demonstrated using a small problem.

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.