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

Emanuele Guerrazzi, Valeria Mininno, Davide Aloini, Riccardo Dulmin, Claudio Scarpelli and Marco Sabatini
Additional contact information
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

Sustainability, 2019, vol. 11, issue 14, 1-15

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.

Keywords: sustainability; energy evaluation; energy recovery; autonomous vehicle storage and retrieval system; simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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