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Energy Evaluation of Deep-Lane Autonomous Vehicle Storage and Retrieval System

Author

Listed:
  • Emanuele Guerrazzi

    (Department of Information Engineering, University of Pisa, Via Girolamo Caruso 16, 56122 Pisa, Italy)

  • Valeria Mininno

    (Department of Energy, Systems, Territory, and Construction Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy)

  • Davide Aloini

    (Department of Energy, Systems, Territory, and Construction Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy)

  • Riccardo Dulmin

    (Department of Energy, Systems, Territory, and Construction Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy)

  • Claudio Scarpelli

    (Department of Energy, Systems, Territory, and Construction Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy)

  • Marco Sabatini

    (Cassioli Group srl, Località Guardavalle 63, 53049 Torrita di Siena, Italy)

Abstract

With the rise of a consciousness in warehousing sustainability, an increasing number of autonomous vehicle storage and retrieval systems (AVS/RS) is diffusing among automated warehouses. Moreover, manufacturers are offering the option of equipping machines with energy recovery systems. This study analyzed a deep-lane AVS/RS provided with an energy recovery system in order to make an energy evaluation for such a system. A simulator able to emulate the operation of the warehouse has been developed, including a travel-time and an energy model to consider the real operating characteristics of lifts, shuttles and satellites. Referring to a single command cycle with a basic storing and picking algorithm for multiple-depth channels, energy balance and recovery measurements have been presented and compared to those of a traditional crane-based system. Results show significant savings in energy consumption with the use of a deep-lane AVS/RS.

Suggested Citation

  • Emanuele Guerrazzi & Valeria Mininno & Davide Aloini & Riccardo Dulmin & Claudio Scarpelli & Marco Sabatini, 2019. "Energy Evaluation of Deep-Lane Autonomous Vehicle Storage and Retrieval System," Sustainability, MDPI, vol. 11(14), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3817-:d:247747
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    References listed on IDEAS

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    Cited by:

    1. Yanyan Wang & Jinning Qin & Shandong Mou & Ke Huang & Xiaofeng Zhao, 2023. "DSS approach for sustainable system design of shuttle-based storage and retrieval systems," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 698-726, September.
    2. Konrad Lewczuk & Michał Kłodawski & Paweł Gepner, 2021. "Energy Consumption in a Distributional Warehouse: A Practical Case Study for Different Warehouse Technologies," Energies, MDPI, vol. 14(9), pages 1-25, May.

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