A genetic-based algorithm with the optimal partition approach for the cell formation in bi-directional linear flow layout
This paper addresses the joint problem of the cell formation and the intercell layout, in which machine cells are located along a linear flow layout. The objective is to minimize the actual intercell flow cost, instead of the typical measure that optimizes the number of intercell movements. A geneti...
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| Published in | International journal of computer integrated manufacturing Vol. 17; no. 4; pp. 364 - 375 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Taylor & Francis Ltd
01.06.2004
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| Online Access | Get full text |
| ISSN | 0951-192X 1362-3052 |
| DOI | 10.1080/09511920310001640512 |
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| Abstract | This paper addresses the joint problem of the cell formation and the intercell layout, in which machine cells are located along a linear flow layout. The objective is to minimize the actual intercell flow cost, instead of the typical measure that optimizes the number of intercell movements. A genetic-based algorithm with optimal partition approach is developed for solving the joint problem. In the proposed approach, the genetic operators are used to generate a pool of solutions, where the solution represents the sequence of machines in the linear layout and each sequence of machines is an individual in the population. The evaluation function, machine cells and its intercell flow cost of each solution are determined by a dynamic programming algorithm, where a sequence of machines is partitioned into several segments or cells, subject to a cell size constraint. In this sense, the genetic algorithm is enhanced with the optimization framework in the proposed approach. The computational efficiency has also been improved since the number of machine cells does not need to be specified in advance in the cell formation approaches. Numerical studies, including 14 data sets adapted from the literature, are performed to demonstrate the viability of the approach. |
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| AbstractList | This paper addresses the joint problem of the cell formation and the intercell layout, in which machine cells are located along a linear flow layout. The objective is to minimize the actual intercell flow cost, instead of the typical measure that optimizes the number of intercell movements. A geneticbased algorithm with optimal partition approach is developed for solving the joint problem. In the proposed approach, the genetic operators are used to generate a pool of solutions, where the solution represents the sequence of machines in the linear layout and each sequence of machines is an individual in the population. The evaluation function, machine cells and its intercell flow cost of each solution are determined by a dynamic programming algorithm, where a sequence of machines is partitioned into several segments or cells, subject to a cell size constraint. In this sense, the genetic algorithm is enhanced with the optimization framework in the proposed approach. The computational efficiency has also been improved since the number of machine cells does not need to be specified in advance in the cell formation approaches. Numerical studies, including 14 data sets adapted from the literature, are performed to demonstrate the viability of the approach. This paper addresses the joint problem of the cell formation and the intercell layout, in which machine cells are located along a linear flow layout. The objective is to minimize the actual intercell flow cost, instead of the typical measure that optimizes the number of intercell movements. A genetic-based algorithm with optimal partition approach is developed for solving the joint problem. In the proposed approach, the genetic operators are used to generate a pool of solutions, where the solution represents the sequence of machines in the linear layout and each sequence of machines is an individual in the population. The evaluation function, machine cells and its intercell flow cost of each solution are determined by a dynamic programming algorithm, where a sequence of machines is partitioned into several segments or cells, subject to a cell size constraint. In this sense, the genetic algorithm is enhanced with the optimization framework in the proposed approach. The computational efficiency has also been improved since the number of machine cells does not need to be specified in advance in the cell formation approaches. Numerical studies, including 14 data sets adapted from the literature, are performed to demonstrate the viability of the approach. |
| Author | Lee, Shine-Der Chiang, Chih-Ping |
| Author_xml | – sequence: 1 givenname: Chih-Ping surname: Chiang fullname: Chiang, Chih-Ping organization: Graduate School of Industrial Engineering and Management , Cheng Shiu University – sequence: 2 givenname: Shine-Der surname: Lee fullname: Lee, Shine-Der organization: Graduate School of Industrial Management , National Cheng Kung University |
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| CitedBy_id | crossref_primary_10_1080_0951192X_2013_814167 crossref_primary_10_1016_j_cor_2012_01_012 crossref_primary_10_1080_0951192X_2011_608718 crossref_primary_10_1108_17410380610688269 crossref_primary_10_1007_s00170_013_5342_x crossref_primary_10_1016_j_asoc_2018_02_021 crossref_primary_10_1080_0951192X_2020_1775300 crossref_primary_10_1080_0951192X_2012_665182 crossref_primary_10_4018_IJAMC_292498 crossref_primary_10_3390_app9224736 crossref_primary_10_1243_09544054JEM382 crossref_primary_10_1016_j_cie_2013_07_009 crossref_primary_10_1080_0951192X_2018_1493229 |
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