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<p>The Assembly Line Balancing Problem (ALBP) is a critical optimization challenge in manufacturing systems that aims to assign tasks to workstations while optimizing some critical objectives. Despite extensive research on single-objective optimization, real-world manufacturing processes often require the simultaneous optimization of multiple objectives. This study focuses on the Simple ALBP of Type 2, with particular focus on multi-objective optimization (MO-SALBP-2), which includes minimizing both cycle time and squared workload. To solve the MO-SALBP-2, the authors propose a novel hybrid approach, HGA-MYSA, which integrates an improved Genetic Algorithm (GA) with Moodie Young heuristic (M&amp;Y) and Simulated Annealing (SA). Furthermore, the authors did not find any work in the literature that aims to combine GA, M&amp;Y, and SA to solve ALBP. The proposed HGA-MYSA includes M&amp;Y as a local improvement phase and SA as a strategic local search mechanism to improve solution quality and avoid local optima. Experimental results on three problems of different sizes demonstrate the superiority of the HGA-MYSA algorithm over conventional GA and Ant Colony Optimization (ACO) algorithms. The HGA-MYSA method achieves lower values for both objectives and exhibits more stable convergence. This improvement is the result of hybridizing the GA with two local improvement and search phases, which ensure better task assignment and maximize line efficiency in most scenarios. These findings highlight the robustness, efficiency, and scalability of this new hybrid approach, making it a promising and better solution for problems of different sizes.</p>

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A novel hybrid genetic algorithm with moodie young heuristic and simulated annealing for multi-objective assembly line balancing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月5日

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  • Sana El Machouti,
  • Mustapha Hlyal,
  • Jamila El Alami

摘要

The Assembly Line Balancing Problem (ALBP) is a critical optimization challenge in manufacturing systems that aims to assign tasks to workstations while optimizing some critical objectives. Despite extensive research on single-objective optimization, real-world manufacturing processes often require the simultaneous optimization of multiple objectives. This study focuses on the Simple ALBP of Type 2, with particular focus on multi-objective optimization (MO-SALBP-2), which includes minimizing both cycle time and squared workload. To solve the MO-SALBP-2, the authors propose a novel hybrid approach, HGA-MYSA, which integrates an improved Genetic Algorithm (GA) with Moodie Young heuristic (M&Y) and Simulated Annealing (SA). Furthermore, the authors did not find any work in the literature that aims to combine GA, M&Y, and SA to solve ALBP. The proposed HGA-MYSA includes M&Y as a local improvement phase and SA as a strategic local search mechanism to improve solution quality and avoid local optima. Experimental results on three problems of different sizes demonstrate the superiority of the HGA-MYSA algorithm over conventional GA and Ant Colony Optimization (ACO) algorithms. The HGA-MYSA method achieves lower values for both objectives and exhibits more stable convergence. This improvement is the result of hybridizing the GA with two local improvement and search phases, which ensure better task assignment and maximize line efficiency in most scenarios. These findings highlight the robustness, efficiency, and scalability of this new hybrid approach, making it a promising and better solution for problems of different sizes.