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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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    3. Riccardo Manzini & Riccardo Accorsi & Giulia Baruffaldi & Teresa Cennerazzo & Mauro Gamberi, 2016. "Travel time models for deep-lane unit-load autonomous vehicle storage and retrieval system (AVS/RS)," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4286-4304, July.
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    5. Bartolini, M. & Bottani, E. & Grosse, E. H., 2019. "Green warehousing: systematic literature review and bibliometric analysis," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 112369, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
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    Cited by:

    1. Rizqi, Zakka Ugih & Chou, Shuo-Yan & Khairunisa, Adinda, 2024. "Energy harvesting for automated storage and retrieval system with sustainable configuration of storage assignment and input/output point," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    2. 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.
    3. 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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