Automated shuttle-based storage and retrieval systems are widely used in warehouses and distribution centers to improve efficiency, accuracy, and space utilization. A special class is represented by the four-way shuttle-based storage and retrieval system, where handling robots can move in four directions (forward, backward, and sideways) within the racking system. This study introduces a methodology to assess energy consumption and the emissions of multiple handling robots in a shuttle-based automated storage and retrieval system. The application of a path planning simulation model assigns the actual routing to shuttles and allows for the computation of the corresponding impacts, according to a set of equations. Moreover, the paper presents the application to a realistic industrial environment using a multi-scenario comparative and competitive analysis. This analysis identifies the most critical energy-efficiency leverages in the path planning of multiple handling robots.

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Impact of Path Planning on Energy Use and Emissions in Four Way Shuttle Storage Systems

  • Marco Ricci,
  • Riccardo Accorsi,
  • Ilaria Battarra,
  • Giacomo Lupi,
  • Riccardo Manzini,
  • Gabriele Sirri

摘要

Automated shuttle-based storage and retrieval systems are widely used in warehouses and distribution centers to improve efficiency, accuracy, and space utilization. A special class is represented by the four-way shuttle-based storage and retrieval system, where handling robots can move in four directions (forward, backward, and sideways) within the racking system. This study introduces a methodology to assess energy consumption and the emissions of multiple handling robots in a shuttle-based automated storage and retrieval system. The application of a path planning simulation model assigns the actual routing to shuttles and allows for the computation of the corresponding impacts, according to a set of equations. Moreover, the paper presents the application to a realistic industrial environment using a multi-scenario comparative and competitive analysis. This analysis identifies the most critical energy-efficiency leverages in the path planning of multiple handling robots.