Assembly line balancing using genetic algorithms

Assembly line balancing using genetic algorithms Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. As small improvements in the performance of the system can lead to significant monetary consequences, it is of utmost importance to develop practical solution procedures that yield high-quality design decisions with minimal computational requirements. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. In this paper, we propose an efficient heuristic to solve the deterministic and single-model ALB problem. The proposed heuristic is a Genetic Algorithm (GA) with a special chromosome structure that is partitioned dynamically through the evolution process. Elitism is also implemented in the model by using some concepts of Simulated Annealing (SA). In this context, the proposed approach can be viewed as a unified framework which combines several new concepts of AI in the algorithmic design. Our computational experiments with the proposed algorithm indicate that it outperforms the existing heuristics on several test problems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Intelligent Manufacturing Springer Journals

Assembly line balancing using genetic algorithms

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Publisher
Springer Journals
Copyright
Copyright © 2000 by Kluwer Academic Publishers
Subject
Business and Management; Production; Manufacturing, Machines, Tools; Control, Robotics, Mechatronics
ISSN
0956-5515
eISSN
1572-8145
D.O.I.
10.1023/A:1008923410076
Publisher site
See Article on Publisher Site

Abstract

Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. As small improvements in the performance of the system can lead to significant monetary consequences, it is of utmost importance to develop practical solution procedures that yield high-quality design decisions with minimal computational requirements. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. In this paper, we propose an efficient heuristic to solve the deterministic and single-model ALB problem. The proposed heuristic is a Genetic Algorithm (GA) with a special chromosome structure that is partitioned dynamically through the evolution process. Elitism is also implemented in the model by using some concepts of Simulated Annealing (SA). In this context, the proposed approach can be viewed as a unified framework which combines several new concepts of AI in the algorithmic design. Our computational experiments with the proposed algorithm indicate that it outperforms the existing heuristics on several test problems.

Journal

Journal of Intelligent ManufacturingSpringer Journals

Published: Oct 5, 2004

References

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