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Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines

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Abstract

Different from a large number of existing studies in the literature, this paper addresses two important issues in managing production lines, the problems of line balancing and model sequencing, concurrently. A novel hybrid agent-based ant colony optimization–genetic algorithm approach is developed for the solution of mixed model parallel two-sided assembly line balancing and sequencing problem. The existing agent-based ant colony optimization algorithm is enhanced with the integration of a new genetic algorithm-based model sequencing mechanism. The algorithm provides ants the opportunity of selecting a random behavior among ten heuristics commonly used in the line balancing domain. A numerical example is given to illustrate the solution building procedure of the algorithm and the evolution of the chromosomes. The performance of the developed algorithm is also assessed through test problems and analysis of their solutions through a statistical test, namely paired sample t test. In accordance with the test results, it is statistically proven that the integrated genetic algorithm-based model sequencing engine helps agent-based ant colony optimization algorithm robustly find significantly better quality solutions.

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Authors and Affiliations

  1. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Streatham Campus, Exeter, England, UK

    Ibrahim Kucukkoc & David Z Zhang

  2. Department of Industrial Engineering, Balikesir University, Cagis Campus, Balikesir, Turkey

    Ibrahim Kucukkoc

Authors
  1. Ibrahim Kucukkoc
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  2. David Z Zhang
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Correspondence to Ibrahim Kucukkoc.

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Kucukkoc, I., Zhang, D.Z. Integrating ant colony and genetic algorithms in the balancing and scheduling of complex assembly lines. Int J Adv Manuf Technol 82, 265–285 (2016). https://doi.org/10.1007/s00170-015-7320-y

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  • DOI: https://doi.org/10.1007/s00170-015-7320-y

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