Research database

The European Environment Agency has recently estimated that about 25% of GreenHouse Gas Emissions (GHGE) in Europe is due to the transportation sector, one of the major sources of environmental pollution. Moreover, the Road Transport (RT) is responsible for about 70% of GHGE due to the transportation sector, emissions forecasted to double in 2050. Therefore, several countries are incentivizing the use of Electric Vehicles (EVs), seen as one promising solution. Moreover, the recent developments in the automotive field have increased the EV drive range (one of the Achilles heels to their diffusion) and allow also partially recharging batteries. For example, a commercial light-duty EV travels without recharging most of a journey in city logistics (distances on average less than 80 km). However, in Mid-Haul Logistics (MHL), where the kilometers covered are usually greater than the drive range of medium-duty commercial EVs, recharges en-route are needed. In addition, Recharging Stations (RSs) are scarcely distributed on the territory, e.g., in Italy, they are concentrated in the North. This may cause (also long) waiting times at RSs that for a company operating in MHL may mean not to serve customers on time, consequently using more EVs leading to an increase in total cost. In this context, SUPERSONIC aims at supporting companies operating in MHL towards an ecological transition, proposing innovative policies to manage public/shared RSs. In all the policies, the availability of a limited number of chargers with possible different kinds of charging technologies (e.g. normal, fast) is considered. In particular, three innovative recharging policies will be introduced, each leading to a different vehicle routing problem: P1 in which RSs are reserved largely in advance, i.e., before starting the trip (leading to time windows at RSs); P2 with an on-line monitoring of the RS occupancy (leading to possible dynamic route changes) and P3 with an instant RS reservation (e.g., at most half an hour in advance by using an app), i.e., after starting the trip unlike P1. In both P2 and P3, uncertain RS availability is considered since, upon EV arrival, it may be no longer vacant. P1, P2 and P3 are also addressed allowing a battery-based collaboration among EVs of the same fleet, i.e., an EV may recharge another, leading to the versions P1’, P2’ and P3’, respectively. P1 and P1’ will be solved by both metaheuristics and matheuristics and bounding techniques to estimate the solution quality will be also proposed. P2, P2’, P3 and P3’ will be addressed both without historical data, by stochastic and robust optimization and with historical data, properly collected, by innovative approaches combining machine/deep learning with mathematical optimization. SUPERSONIC will contribute to promote smarter RSs with a consequent time savings for companies operating in MHL and to incentivize the use of EVs with consequent positive impacts from a socio-economic point of view.