Latest revision as of 15:40, 26 January 2021

Abstract

Traditional car-sharing services are based on the two-way scheme, where the user picks up and returns the vehicle at the same parking station. Some services also allow one-way trips, where the user can return the vehicle in a different station. The one-way scheme is more attractive for users, but may pose a problem in the distribution of the vehicles, due to a possible unbalanced equation between the user’s demand and the availability of vehicles or free slots at the stations. This issue becomes more complex in the case of electric car sharing, where the travel range depends on the state-of-charge of the vehicles. In a previous work, Bruglieri et al. introduced a new approach to relocate the vehicles where cars were moved by personnel of the service operator to keep the system balanced. Such relocation method generates a new challenging pickup and delivery problem called Electric Vehicle Relocation Problem (E-VReP). Consequently, Bruglieri et al. introduced a method to forecast the unbalancing of a car-sharing system. They applied such method to the data yielded by the Milan transport agency, taking into account the location and capacity of charging stations in Milan. Finally, some years later, they introduced the economic sustainability of the relocation approach, introducing a revenue associated to each request, and a cost associated with each operator. In the proposed new model, authors want to add two new features to the E-VReP. The first one is the implementation of a working day with two work shifts. Until now each operator could start working at different times. With this new feature, we set two shifts each day, one in the morning, and one in the afternoon. The second novelty is the possibility for operators to collaborate among them. This means that operators can carpool in an EV in order to reach their destinations faster than if they tried reaching it by bike. In this way, using a Mixed Integer Linear Programming (MILP) formulation of E-VReP, we can estimate the advantages of our relocation approach on verisimilar instances.

Outgoing

Original document

The different versions of the original document can be found in:

• http://hdl.handle.net/2117/167449 under the license http://creativecommons.org/licenses/by-nc-nd/3.0/es/