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<p>As the aerospace industry advances and demand grows, production departments are seeking optimal manufacturing methods to increase throughput and reduce costs. This study aims to optimize the configuration of automated guided vehicles (AGVs) in aerospace assembly workshops to enhance production efficiency. A production simulation model was developed using discrete event simulation, incorporating key elements such as workstations, AGV delivery systems, and charging stations. Optimization experiments were conducted using the design of experiments methodology to evaluate the impact of varying AGV quantities and speed configurations on production capacity, workstation utilization, and bottleneck shifts. The optimal AGV configuration was identified, leading to significant improvements in production efficiency. The robustness of the production system was also assessed by accounting for variations in assembly time and machine breakdowns. This research provides valuable guidance for AGV configuration and production simulation validation in smart manufacturing environments.</p>

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Production simulation and configuration optimization based on discrete event simulation: A case study of an aerospace assembly workshop

  • 期刊论文

  • 发表日期：

    2025年3月31日

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  • Shengluo Yang,
  • Shuoxin Yin,
  • Junyi Wang,
  • Weidong Li,
  • Zhigang Xu

摘要

As the aerospace industry advances and demand grows, production departments are seeking optimal manufacturing methods to increase throughput and reduce costs. This study aims to optimize the configuration of automated guided vehicles (AGVs) in aerospace assembly workshops to enhance production efficiency. A production simulation model was developed using discrete event simulation, incorporating key elements such as workstations, AGV delivery systems, and charging stations. Optimization experiments were conducted using the design of experiments methodology to evaluate the impact of varying AGV quantities and speed configurations on production capacity, workstation utilization, and bottleneck shifts. The optimal AGV configuration was identified, leading to significant improvements in production efficiency. The robustness of the production system was also assessed by accounting for variations in assembly time and machine breakdowns. This research provides valuable guidance for AGV configuration and production simulation validation in smart manufacturing environments.