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<p>Modern assembly line optimization requires maximizing both productivity and quality while taking into account dynamic constraints related to operator skills, a critical challenge in today’s rapidly evolving manufacturing environments. This study introduces a new bi-objective Assembly Line Balancing (ALB) model that is capable of adapting in real time to operational changes in operator skills through continuous learning mechanisms. The model simultaneously aims to maximize production output and minimize defect rates. The proposed technique automatically updates operator skill matrices using historical performance data and real-time production feedback, creating a closed-loop improvement system. This innovative approach effectively replaces subjective supervisor decisions with systematic, data-driven resource allocation based on quantitative performance metrics. Our mathematical formulation resolves the critical gap between theoretical ALB assumptions and industrial practice by explicitly incorporating time-varying operator skills, production quality trade-offs, and demand-resource variability. The model offers manufacturers a dynamic balancing mechanism that facilitates adaptive decision-making, enhances line agility, and optimizes operational efficiency. Additionally, it contributes to the development of workforce skills.</p>

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Dynamic bi-objective balancing of assembly lines: optimizing productivity and quality by integrating operator skill evolution

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月5日

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  • Mustapha Hlyal,
  • Sana El Machouti

摘要

Modern assembly line optimization requires maximizing both productivity and quality while taking into account dynamic constraints related to operator skills, a critical challenge in today’s rapidly evolving manufacturing environments. This study introduces a new bi-objective Assembly Line Balancing (ALB) model that is capable of adapting in real time to operational changes in operator skills through continuous learning mechanisms. The model simultaneously aims to maximize production output and minimize defect rates. The proposed technique automatically updates operator skill matrices using historical performance data and real-time production feedback, creating a closed-loop improvement system. This innovative approach effectively replaces subjective supervisor decisions with systematic, data-driven resource allocation based on quantitative performance metrics. Our mathematical formulation resolves the critical gap between theoretical ALB assumptions and industrial practice by explicitly incorporating time-varying operator skills, production quality trade-offs, and demand-resource variability. The model offers manufacturers a dynamic balancing mechanism that facilitates adaptive decision-making, enhances line agility, and optimizes operational efficiency. Additionally, it contributes to the development of workforce skills.