The paper reports the results of a research targeted to develop a Decision Support System (DSS) for planning and operation of urban deliveries carried out with electric vans. The research was included within the 2019-21 Research Program for the Electric System, coordinated by the Italian Ministry for the Ecological Transition, and has been performed by ENEA, the Italian Agency for Energy, New Technologies and Sustainable Development, and “La Sapienza” University of Rome. The new DSS is based on meta-heuristics algorithms capable to manage a generic set of goods to be delivered by means of a generic fleet of electric vans, with the objective of minimizing the overall cost of the daily operation. The algorithm considers all the physical constraints, including vehicles batteries capacity. It is assumed that fast recharges can be performed during the delivery tours. For the real-time operation, a monitoring system of the vehicle fleet, road network and recharge stations is assumed, based on IoT technologies, in order to detect possible unexpected events and manage them in the best way, according to the available resources time by time. The paper describes the DSS general architecture, the optimization algorithms and the recovery procedures and shows results for two testbeds.

A platform to optimize urban deliveries with e-vans Dealing with vehicles range and batteries recharge / Valentini, Maria Pia; Carrese, Filippo; Colombaroni, Chiara; Conti, Valentina; Corazza, Matteo; Lelli, Maria; Mohammadi, Mostafa; Orchi, Silvia; Ortenzi, Fernando; Rahmanifar, Golman; Tomasino, FIORE GIUSEPPE; Fusco, Gaetano. - In: TEMA. - ISSN 1970-9889. - 16:2(2023), pp. 403-423. [10.6093/1970-9870/9911]

A platform to optimize urban deliveries with e-vans Dealing with vehicles range and batteries recharge

Filippo, Carrese;Chiara, Colombaroni;Mostafa, Mohammadi;Fernando, Ortenzi;Golman, Rahmanifar;Giuseppe, Tomasino;Gaetano, Fusco
2023

Abstract

The paper reports the results of a research targeted to develop a Decision Support System (DSS) for planning and operation of urban deliveries carried out with electric vans. The research was included within the 2019-21 Research Program for the Electric System, coordinated by the Italian Ministry for the Ecological Transition, and has been performed by ENEA, the Italian Agency for Energy, New Technologies and Sustainable Development, and “La Sapienza” University of Rome. The new DSS is based on meta-heuristics algorithms capable to manage a generic set of goods to be delivered by means of a generic fleet of electric vans, with the objective of minimizing the overall cost of the daily operation. The algorithm considers all the physical constraints, including vehicles batteries capacity. It is assumed that fast recharges can be performed during the delivery tours. For the real-time operation, a monitoring system of the vehicle fleet, road network and recharge stations is assumed, based on IoT technologies, in order to detect possible unexpected events and manage them in the best way, according to the available resources time by time. The paper describes the DSS general architecture, the optimization algorithms and the recovery procedures and shows results for two testbeds.
2023
urban deliveries; electric vans; decision support system
01 Pubblicazione su rivista::01a Articolo in rivista
A platform to optimize urban deliveries with e-vans Dealing with vehicles range and batteries recharge / Valentini, Maria Pia; Carrese, Filippo; Colombaroni, Chiara; Conti, Valentina; Corazza, Matteo; Lelli, Maria; Mohammadi, Mostafa; Orchi, Silvia; Ortenzi, Fernando; Rahmanifar, Golman; Tomasino, FIORE GIUSEPPE; Fusco, Gaetano. - In: TEMA. - ISSN 1970-9889. - 16:2(2023), pp. 403-423. [10.6093/1970-9870/9911]
File allegati a questo prodotto
File Dimensione Formato  
Valentini_A-platform-to-optimize_2023.pdf

accesso aperto

Note: articolo
Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Creative commons
Dimensione 2.4 MB
Formato Adobe PDF
2.4 MB Adobe PDF

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1686630
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 6
  • ???jsp.display-item.citation.isi??? ND
social impact