Results 1  10
of
10
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 ..."
Abstract

Cited by 15 (5 self)
 Add to MetaCart
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.
Grouping Components In Printed Circuit Board Assembly with Limited . . .
 INT. J. PROD. RES.
, 1997
"... ..."
Integrating Design And Planning Considerations In Cellular Manufacturing
"... This paper presents a new model that integrates design and planning to prescribe a costeffective 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, multip ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
This paper presents a new model that integrates design and planning to prescribe a costeffective 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.
unknown title
"... Grouping components in printed circuit board assembly with limited component staging capacity and single card setup: problem characteristics and solution procedures ..."
Abstract
 Add to MetaCart
Grouping components in printed circuit board assembly with limited component staging capacity and single card setup: problem characteristics and solution procedures
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 ..."
Abstract
 Add to MetaCart
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.
www.elsevier.com/locate/dsw An evolutionary algorithm for manufacturing cell formation *
, 2003
"... 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 ..."
Abstract
 Add to MetaCart
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. q 2004 Published by Elsevier Ltd.
unknown title
, 2003
"... www.elsevier.com/locate/dsw Similarity coefficient methods applied to the cell formation problem: a comparative investigation ..."
Abstract
 Add to MetaCart
www.elsevier.com/locate/dsw Similarity coefficient methods applied to the cell formation problem: a comparative investigation
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 ..."
Abstract
 Add to MetaCart
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.
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 ..."
Abstract
 Add to MetaCart
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.