The Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and Recharging Stations

Abstract

Due to new regulations and further technological progress in the field of electric vehicles, the research community faces the new challenge of incorporating the electric energy based restrictions into vehicle routing problems. One of these restrictions is the limited battery capacity which makes detours to recharging stations necessary, thus requiring efficient tour planning mechanisms in order to sustain the competitiveness of electric vehicles compared to conventional vehicles. We introduce the Electric Fleet Size and Mix Vehicle Routing Problem with Time Windows and recharging stations (E-FSMFTW) to model decisions to be made with regards to fleet composition and the actual vehicle routes including the choice of recharging times and locations. The available vehicle types differ in their transport capacity, battery size and acquisition cost. Furthermore, we consider time windows at customer locations, which is a common and important constraint in real-world routing and planning problems. We solve this problem by means of branch-and-price as well as proposing a hybrid heuristic, which combines an Adaptive Large Neighbourhood Search with an embedded local search and labelling procedure for intensification. By solving a newly created set of benchmark instances for the E-FSMFTW and the existing single vehicle type benchmark using an exact method as well, we show the effectiveness of the proposed approach.

Electric Vehicles in Logistics and Transportation: A Survey on Emerging Environmental, Strategic, and Operational Challenges

“…It is in fact reflected in the range parameter of the vehicle which is simply doubled, meaning that the battery of the EV can be swapped virtually anywhere on the road. Hiermann et al [70] developed that idea further to propose an electrical vehicle fleet size and mix VRP with time windows that also considers the decisions regarding the fleet composition and the choice of recharging times and locations. This work can be considered as the state of the art of delivery optimization with EVs.…”

“…As described in [70], Liu and Shen [93] proposes the fleet size and mix VRP with time windows reformulating the FSMF to take into account time windows. The routing cost corresponds to the so-called en route time , which is the time between departing from and returning to the depot menus de cumulative service time at the customers in the respective route.…”

The multi-depot electric vehicle location routing problem with time windows

“…The optimization objective used is hierarchical, in which the number of vehicles used to achieve all the customers' demands is minimized, and then the total distance traveled is minimized. This work was then extended by Hiermann et al (2016) considering a heterogeneous fleet of EVs. The optimization objective was the overall distance and the acquisition costs of the vehicles.…”

Stochastic Navigation in Smart Cities

“…Combining the problem with a multi-agent architecture is shown to provide good results [4]. In [5,6] the authors present different types of hybrid heuristics to solve the electric vehicle-routing problem with time windows and charging stations. However, none of these works considers the problem of finding a charging station in case of partial information.…”