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Manufacturing Cell Design: An Integer Programming Model Employing Genetic Algorithms
 IIE Transactions
, 1996
"... 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 le ..."
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Cited by 16 (5 self)
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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 intercell 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.
Utilizing Lamarckian Evolution and the Baldwin Effect in Hybrid Genetic Algorithms
, 1996
"... 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 ..."
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Cited by 3 (1 self)
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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...
unknown title
, 2003
"... www.elsevier.com/locate/dsw Similarity coefficient methods applied to the cell formation problem: a comparative investigation ..."
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www.elsevier.com/locate/dsw Similarity coefficient methods applied to the cell formation problem: a comparative investigation
Concurrent Cell Formation for Cellular Manufacturing System by Preemptive Fuzzy Goal Programming
"... Abstract—A new concurrent cell formation method for solving a Cell Formation (CF) problem in a Cellular Manufacturing System (CMS) is developed and proposed in this research. To solve such problem, conventionally a facility planner needs to classify parts into families and group machines into cells, ..."
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Abstract—A new concurrent cell formation method for solving a Cell Formation (CF) problem in a Cellular Manufacturing System (CMS) is developed and proposed in this research. To solve such problem, conventionally a facility planner needs to classify parts into families and group machines into cells, respectively. However, existing methods for solving the CF problem are difficult and complicated. Moreover, efficient solutions of some of those methods are not guarantee. So, the efficient method based on two important performance measures, called Exceptional Elements (EE) and the Void Elements (VE) of a perfect grouping, are developed. Preemptive Fuzzy Goal Programming (PFGP) is applied to these two performance measures for finding the efficient solution. The problems of grouping part family and machine cells can be simultaneously easily solved. Moreover, machines and parts grouping can also be adjustable to find preferred solutions by use of PFGP. The numerical examples existed in the literatures are shown to demonstrate the efficiency of the proposed model over the conventional method.
DOI 10.1007/s0017000320485 ORIGINAL ARTICLE
"... Abstract The primary objective of group technology (GT) is to enhance the productivity in the batch manufacturing environment. The GT cell formation problem is solved using modified binary adaptive resonance theory networks known as ART1. The input to the modified ART1 is a machinepart incidence ma ..."
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Abstract The primary objective of group technology (GT) is to enhance the productivity in the batch manufacturing environment. The GT cell formation problem is solved using modified binary adaptive resonance theory networks known as ART1. The input to the modified ART1 is a machinepart incidence matrix comprised of the binary digits “0 ” and “1”. And the outputs are the list of part families and the corresponding part list, machine cells and their corresponding list of machines, and the number of exceptional elements. This method is applied to the known benchmarked problems found in the literature and it is found to outperform other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented.
Performance of Fuzzy ART neural network and hierarchical clustering
"... for part–machine grouping based on operation sequences ..."
DOI 10.1007/s001700042421z ORIGINAL ARTICLE
"... that attempts to reduce production cost by reducing the material handling and transportation cost. The GT cell formation by any known algorithm/heuristics results in much intercell movement known as exceptional elements. In such cases, fractional cell formation using reminder cells can be adopted su ..."
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that attempts to reduce production cost by reducing the material handling and transportation cost. The GT cell formation by any known algorithm/heuristics results in much intercell movement known as exceptional elements. In such cases, fractional cell formation using reminder cells can be adopted successfully to minimize the number of exceptional elements. The fractional cell formation problem is solved using modified adaptive resonance theory1 network (ART1). The input to the modified ART1 is machinepart incidence matrix comprising of the binary digits 0 and 1. This method is applied to the known benchmarked problems found in the literature and it is found to be equal or superior to other algorithms in terms of minimizing the number of the exceptional elements. The relative merits of using this method with respect to other known algorithms/heuristics in terms of computational speed and consistency are presented.
A Hybrid Genetic Algorithm for Manufacturing Cell Formation
"... 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 fo ..."
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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 TD5FE6RN, October 29, 2002.