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

Ahmed Mellouli (), Racem Mellouli, Hager Triki and Faouzi Masmoudi
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Ahmed Mellouli: University of Sousse
Racem Mellouli: University of Sfax
Hager Triki: University of Sfax
Faouzi Masmoudi: University of Sfax

Annals of Operations Research, 2025, vol. 351, issue 1, No 33, 903-935

Abstract: 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.

Keywords: Assembly line balancing problem; Ant colony; Genetic algorithm; Cycle time; Productivity; Metaheuristics (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-024-06071-9

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