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<p>In the era of Industry 4.0, collaborative robots (cobots) have emerged as pivotal technologies reshaping the manufacturing landscape. The collaboration between humans and cobots brings higher productivity and flexibility. Specifically, cobots mitigate physical overload by assuming repetitive and labor-intensive tasks, thereby improving worker safety and reducing fatigue. This study attempts to quantify ergonomics in human–robot collaborative two-sided assembly line balancing problems (HRCTALBP) through a fatigue-and-recovery criterion. Furthermore, in light of global energy sustainability concerns, cobot energy consumption is integrated as a critical optimization parameter. To address these challenges, a multi-objective framework is proposed that simultaneously minimizes cycle time, ergonomic risk, and energy consumption. A novel mixed-integer programming model is formulated, incorporating multi-skilled workers and adaptive cobot capabilities. In addition, an improved multi-objective migrating birds optimization algorithm is developed, featuring a multi-population co-evolution mechanism to enhance solution diversity and convergence. The algorithm’s efficacy is rigorously validated against three different algorithms through comparative experiments. Finally, a real-world case study from the automotive industry further demonstrates the practicality of the proposed approach. Results highlight HRC as an ergonomic and efficient solution, while the derived Pareto-optimal solutions offer actionable insights for production managers in configuring assembly lines that balance productivity, worker well-being, and sustainability.</p>

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Multi-objective human–robot collaborative two-sided assembly line balancing problem with the optimization of ergonomics and energy consumption

  • 期刊论文

  • 发表日期：

    2026年4月27日

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  • Nan Chen,
  • Jing Ning,
  • Fajun Yang,
  • Di Wang

摘要

In the era of Industry 4.0, collaborative robots (cobots) have emerged as pivotal technologies reshaping the manufacturing landscape. The collaboration between humans and cobots brings higher productivity and flexibility. Specifically, cobots mitigate physical overload by assuming repetitive and labor-intensive tasks, thereby improving worker safety and reducing fatigue. This study attempts to quantify ergonomics in human–robot collaborative two-sided assembly line balancing problems (HRCTALBP) through a fatigue-and-recovery criterion. Furthermore, in light of global energy sustainability concerns, cobot energy consumption is integrated as a critical optimization parameter. To address these challenges, a multi-objective framework is proposed that simultaneously minimizes cycle time, ergonomic risk, and energy consumption. A novel mixed-integer programming model is formulated, incorporating multi-skilled workers and adaptive cobot capabilities. In addition, an improved multi-objective migrating birds optimization algorithm is developed, featuring a multi-population co-evolution mechanism to enhance solution diversity and convergence. The algorithm’s efficacy is rigorously validated against three different algorithms through comparative experiments. Finally, a real-world case study from the automotive industry further demonstrates the practicality of the proposed approach. Results highlight HRC as an ergonomic and efficient solution, while the derived Pareto-optimal solutions offer actionable insights for production managers in configuring assembly lines that balance productivity, worker well-being, and sustainability.