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.

Energy evaluation of deep-lane autonomous vehicle storage and retrieval system

GUERRAZZI, EMANUELE;Mininno V.;Aloini D.;Dulmin R.;SCARPELLI, CLAUDIO;
2019-01-01

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.
2019
Guerrazzi, Emanuele; Mininno, V.; Aloini, D.; Dulmin, R.; Scarpelli, Claudio; Sabatini, M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/1000785
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