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An evolutionary algorithm for manufacturing cell formation
, 2004
"... Cellular manufacturing emerged as a production strategy capable of solving the certain 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 app ..."
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Cited by 27 (13 self)
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Cellular manufacturing emerged as a production strategy capable of solving the certain 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.
Empirical Investigation of the Benefits of Partial Lamarckianism
, 1997
"... Genetic algorithms (GAs) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, which are good at ..."
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Cited by 25 (2 self)
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Genetic algorithms (GAs) are very efficient at exploring the entire search space; however, they are relatively poor at finding the precise local optimal solution in the region in which the algorithm converges. Hybrid genetic algorithms are the combination of improvement procedures, which are good at finding local optima, and genetic algorithms. There are two basic strategies for using hybrid GAs. In the first, Lamarckian learning, the genetic representation is updated to match the solution found by the improvement procedure. In the second, Baldwinian learning, improvement procedures are used to change the fitness landscape, but the solution that is found is not encoded back into the genetic string. This paper examines the issue of using partial Lamarckianism, i.e., the updating of the genetic representation for only a percentage of the individuals, as compared to pure Lamarckian and pure Baldwinian learning in hybrid GAs. Multiple instances of five bounded nonlinear problems, the locat...
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 24 (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 5 (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...
MANUFACTURING CELL DESIGN WITH REDUCTION IN SETUP TIME THROUGH GENETIC ALGORITHM
"... 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 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 HybridGenetic Algorithm for Manufacturing Cell Design
, 1997
"... Global competition is demanding innovative ways of achieving manufacturing flexibility and reduced costs. One approach is through cellular manufacturing, an implementation of the concepts of group technology. The design of a cellular manufacturing system requires that a part population be at least m ..."
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Global competition is demanding innovative ways of achieving manufacturing flexibility and reduced costs. One approach is through cellular manufacturing, an implementation of the concepts of group technology. The design of a cellular manufacturing system requires that a part population be at least minimally described by its use of process technology (part/machine incidence matrix), and 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. This paper presents a stochastic global optimization technique utilizing genetic algorithms (GAs) and local improvement procedures to solve the cell formation problem. The combination of local improvement procedures with GAs is shown to improve the performance of the GA in terms of quality of solution and computational efficiency. Several different incorporation me...
Moving Beyond the Parts Incidence Matrix: Alternative Routings and Operations for the Cell Formation Problem
"... An integerbased genetic algorithm (GA) to assist in the design of cellular manufacturing (CM) systems is presented. It allows for a variety of evaluation functions and the selective incorporation/exclusion of design constraints during cell formation. In this paper, the GA model developed by Joines ..."
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An integerbased genetic algorithm (GA) to assist in the design of cellular manufacturing (CM) systems is presented. It allows for a variety of evaluation functions and the selective incorporation/exclusion of design constraints during cell formation. In this paper, the GA model developed by Joines et al. [10] is extended to consider alternative operations and machine redundancy as well as completely fixed alternative routes. The approach is demonstrated on data from the literature and is shown to be an effective cell design tool. An industrial case study is described where the clustering problem was solved using the genetic search presented in this paper. The resulting cell design is being implemented. 1 Introduction In cellular manufacturing, the manufacturing system is decomposed into several manageable subsystems, or groups, by aggregating similar parts into part families and dissimilar machines into cells [17]. The ideal cell (1) is independent, i.e., part family(s) are complete...
A Hybrid Genetic Algorithm for Manufacturing Cell Formation
, 2002
"... 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 ..."
Abstract
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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.