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

1
Department of Information Engineering, University of Pisa, Via Girolamo Caruso 16, 56122 Pisa, Italy
2
Department of Energy, Systems, Territory, and Construction Engineering, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy
3
Cassioli Group srl, Località Guardavalle 63, 53049 Torrita di Siena, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(14), 3817; https://doi.org/10.3390/su11143817
Received: 10 June 2019 / Revised: 3 July 2019 / Accepted: 10 July 2019 / Published: 12 July 2019
(This article belongs to the Special Issue Product Innovation and Sustainability)
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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. View Full-Text
Keywords: sustainability; energy evaluation; energy recovery; autonomous vehicle storage and retrieval system; simulation sustainability; energy evaluation; energy recovery; autonomous vehicle storage and retrieval system; simulation
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Guerrazzi, E.; Mininno, V.; Aloini, D.; Dulmin, R.; Scarpelli, C.; Sabatini, M. Energy Evaluation of Deep-Lane Autonomous Vehicle Storage and Retrieval System. Sustainability 2019, 11, 3817.

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