Scholarlink

Factory layout planning involves allocating resources and arranging equipment in manufacturing facilities to enhance system performance and ensure a safe work environment. Integrating digital human modeling tools into factory layout planning facilitates early worker well-being analysis, mitigating musculoskeletal disorders. This paper presents methods for modeling factory layout planning as a multi-objective reinforcement learning problem, leveraging digital human modeling-based simulations.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Reinforcement Learning and Digital Human Modeling for Multi-objective Factory Layout Planning

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Veeresh Elango,
  • Andreas Lind,
  • Manu Sathyarajan Joseph,
  • Akanksha Makkar,
  • Johan Sandblad,
  • Lars Hanson,
  • Anna Syberfeldt,
  • Mikael Forsman

摘要

Factory layout planning involves allocating resources and arranging equipment in manufacturing facilities to enhance system performance and ensure a safe work environment. Integrating digital human modeling tools into factory layout planning facilitates early worker well-being analysis, mitigating musculoskeletal disorders. This paper presents methods for modeling factory layout planning as a multi-objective reinforcement learning problem, leveraging digital human modeling-based simulations.