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Multi-Objective Optimization of an Assembly Layout Using Nature-Inspired Algorithms and a Digital Human Modeling

Andreas Lind1,2, V Elango1,2, L Hanson2

  • 1Scania CV AB, Södertälje, Sweden.

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Summary
This summary is machine-generated.

This study introduces an automated factory layout planning method for Industry 5.0, integrating multi-objective optimization and digital human modeling to enhance worker well-being and system efficiency.

Keywords:
Multi-objectiveassemblyfactory layoutsindustry 5.0optimization

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Area of Science:

  • Manufacturing Engineering
  • Operations Research
  • Human Factors Engineering

Background:

  • Traditional factory layout planning is slow and prone to human error.
  • Existing methods often rely heavily on subjective engineer expertise.
  • Industry 5.0 demands more integrated and efficient planning approaches.

Purpose of the Study:

  • To develop an advanced methodology for manufacturing factory layout planning.
  • To integrate multi-objective optimization with nature-inspired algorithms and digital human modeling.
  • To address limitations of traditional planning methods in Industry 5.0 contexts.

Main Methods:

  • Utilized multi-objective optimization focusing on worker well-being and system performance.
  • Incorporated nature-inspired algorithms for efficient search and optimization.
  • Employed a digital human modeling tool for realistic simulation and analysis.

Main Results:

  • Demonstrated a transparent, cross-disciplinary, and automated layout planning process.
  • Successfully applied the methodology to a pedal car assembly station layout case.
  • Achieved objective and efficient layout planning considering dual targets.

Conclusions:

  • The proposed methodology represents a significant advancement in manufacturing factory layout design.
  • It offers robust multi-objective decision support for factory planning.
  • Facilitates a transition towards more automated and data-driven layout design practices.