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An efficient hybridization of ant colony optimization and genetic algorithm for an assembly line balancing problem of type II under zoning constraints

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  • Ahmed Mellouli

    (University of Sousse
    University of Sfax)

  • Racem Mellouli

    (University of Sfax)

  • Hager Triki

    (University of Sfax
    University of Sfax)

  • Faouzi Masmoudi

    (University of Sfax)

Abstract

This study presents a particular case of type II assembly line balancing problem with task restrictions (TRALBP-2) in which the assembly tasks have to be assigned to workstations under precedence and zoning constraints. The objective is to minimize the cycle time for a fixed number of workstations. For a quick and efficient solution approach of this problem variant, we have developed a hybridization of two metaheuristics: the ant colony optimization and the genetic algorithm. This was motivated by the potential gain of merging the performances and strength levers of the two methods in terms of diversification and intensification to better escape convergence in local optima. The effectiveness of this approach was determined through various set of instances including those randomly generated, retrieved from the literature, and taken from a real-case study of an automotive cable company. The computational results reveal that the proposed method outperforms within reasonable time the existing solutions found in the literature.

Suggested Citation

  • Ahmed Mellouli & Racem Mellouli & Hager Triki & Faouzi Masmoudi, 2025. "An efficient hybridization of ant colony optimization and genetic algorithm for an assembly line balancing problem of type II under zoning constraints," Annals of Operations Research, Springer, vol. 351(1), pages 903-935, August.
    • Handle: RePEc:spr:annopr:v:351:y:2025:i:1:d:10.1007_s10479-024-06071-9
    DOI: 10.1007/s10479-024-06071-9
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