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      Assembly line balancing using genetic algorithms

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      Author(s)
      Sabuncuoğlu İ.
      Erel, E.
      Tanyer, M.
      Date
      2000
      Source Title
      Journal of Intelligent Manufacturing
      Print ISSN
      0956-5515
      Publisher
      Kluwer Academic Publishers
      Volume
      11
      Issue
      3
      Pages
      295 - 310
      Language
      English
      Type
      Article
      Item Usage Stats
      203
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      1,068
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      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.
      Keywords
      Artificial intelligence
      Assembly machines
      Computational complexity
      Computer aided manufacturing
      Genetic algorithms
      Heuristic methods
      Job analysis
      Simulated annealing
      Assembly line balancing (ALB)
      Elitism
      Production control
      Permalink
      http://hdl.handle.net/11693/25037
      Published Version (Please cite this version)
      http://dx.doi.org/10.1023/A:1008923410076
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      • Department of Industrial Engineering 758
      • Department of Management 639
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