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Handling the assembly line balancing problem in the clothing industry using a genetic algorithm

Handling the assembly line balancing problem in the clothing industry using a genetic algorithm Assembly line balancing problems that occur in real world situations are dynamic and are fraught with various sources of uncertainties such as the performance of workers and the breakdown of machinery. This is especially true in the clothing industry. The problem cannot normally be solved deterministically using existing techniques. Recent advances in computing technology, especially in the area of computational intelligence, however, can be used to alleviate this problem. For example, some techniques in this area can be used to restrict the search space in a combinatorial problem, thus opening up the possibility of obtaining better results. Among the different computational intelligence techniques, genetic algorithms (GA) is particularly suitable. GAs are probabilistic search methods that employ a search technique based on ideas from natural genetics and evolutionary principles. In this paper, we present the details of a GA and discuss the main characteristics of an assembly line balancing problem that is typical in the clothing industry. We explain how such problems can be formulated for genetic algorithms to solve. To evaluate the appropriateness of the technique, we have carried out some experiments. Our results show that the GA approach performs much better than the use of a greedy algorithm, which is used by many factory supervisors to tackle the assembly line balancing problem. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Clothing Science and Technology Emerald Publishing

Handling the assembly line balancing problem in the clothing industry using a genetic algorithm

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References (17)

Publisher
Emerald Publishing
Copyright
Copyright © 1998 MCB UP Ltd. All rights reserved.
ISSN
0955-6222
DOI
10.1108/09556229810205240
Publisher site
See Article on Publisher Site

Abstract

Assembly line balancing problems that occur in real world situations are dynamic and are fraught with various sources of uncertainties such as the performance of workers and the breakdown of machinery. This is especially true in the clothing industry. The problem cannot normally be solved deterministically using existing techniques. Recent advances in computing technology, especially in the area of computational intelligence, however, can be used to alleviate this problem. For example, some techniques in this area can be used to restrict the search space in a combinatorial problem, thus opening up the possibility of obtaining better results. Among the different computational intelligence techniques, genetic algorithms (GA) is particularly suitable. GAs are probabilistic search methods that employ a search technique based on ideas from natural genetics and evolutionary principles. In this paper, we present the details of a GA and discuss the main characteristics of an assembly line balancing problem that is typical in the clothing industry. We explain how such problems can be formulated for genetic algorithms to solve. To evaluate the appropriateness of the technique, we have carried out some experiments. Our results show that the GA approach performs much better than the use of a greedy algorithm, which is used by many factory supervisors to tackle the assembly line balancing problem.

Journal

International Journal of Clothing Science and Technology – Emerald Publishing

Published: Mar 1, 1998

Keywords: Assembly line balancing; Clothing industry; Computer modelling

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