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<p>This study integrates the learning rate factor into the design of a new production line, thereby increasing the complexity and enhancing the realism of the line balancing problem. Given the NP-hard nature of line balancing problems, a heuristic method termed rank positional weight with learning rate (RPW-LeRa) was developed to address this challenge. Experiments were conducted utilizing several well-established datasets, and the results of the proposed method were compared with alternative approaches, including RPW without the learning rate, methods with a predefined maximum number of training sessions, and the lower bound approach. The findings indicate that the proposed method significantly reduces the number of required workstations, decreases total costs, and provides surplus time potential to accommodate the forgetting rate phenomenon. This method not only effectively addresses task assignment to workstations, job sequencing, and determination of the optimal number of workstations, but also offers insights into the optimal number of training sessions required.</p>

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Integrating the Learning Rate Curve into Production Line Design: A Cost-Effective Heuristic Approach for Enhanced Line Balancing

  • 期刊论文

  • 发表日期：

    2025年6月13日

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  • Esam Alhomaidi,
  • Hudaifah Hudaifah,
  • Haitham Saleh,
  • Anas Alghazi,
  • Yasser Almoghathawi,
  • Khaled Al-Shareef

摘要

This study integrates the learning rate factor into the design of a new production line, thereby increasing the complexity and enhancing the realism of the line balancing problem. Given the NP-hard nature of line balancing problems, a heuristic method termed rank positional weight with learning rate (RPW-LeRa) was developed to address this challenge. Experiments were conducted utilizing several well-established datasets, and the results of the proposed method were compared with alternative approaches, including RPW without the learning rate, methods with a predefined maximum number of training sessions, and the lower bound approach. The findings indicate that the proposed method significantly reduces the number of required workstations, decreases total costs, and provides surplus time potential to accommodate the forgetting rate phenomenon. This method not only effectively addresses task assignment to workstations, job sequencing, and determination of the optimal number of workstations, but also offers insights into the optimal number of training sessions required.