Abstract
The recent incorporation of Electric Vehicles (EVs) in the worldwide delivery vehicle fleet has increased the importance of their operation in road networks. In particular, the growth of electronic commerce, while promoting the use of non-polluting energy sources, will make EVs play an important role in cities. Thus, an accurate characterization of the technical aspects of EVs and parcel delivery requirements in delivery route operational planning models is required. To that end, this paper presents an enhanced operational planning model for the route and charging of an EV fleet considering technical and economic real-world constraints, such as battery degradation, acceleration- and speed-dependent power consumption, tolls and penalty for delivery delay and non-fulfillment. Moreover, unlike some previous works, the delivery allocation and the number of EVs used are not predetermined, being rather outcomes of the optimization process. Additionally, the Proposed Approach (PA) allows EVs to pass through intersections more than once. The proposed model is formulated in terms of a series of intersection- and path-related decision variables, characterizing the State of Charge (SOC) of batteries and the navigation time. The resulting optimization problem is cast as an instance of Mixed-Integer Linear Programming (MILP). The model is implemented in the mathematical programming language GAMS and solved using the commercial solvers CPLEX and ODH-CPLEX. The model is tested on two intersection maps, including different types of roads, charging rates and delivery points. Results show the effectiveness of the PA over previously reported approaches in terms of the cost, energy and time associated with the resulting operating strategies.
Highlights
The motivation, literature review, contributions and paper organization are presented.A
From a modeling perspective, an enhanced operational planning model for the route and charging of an Electric Vehicles (EVs) fleet is presented, where the delivery allocation and the number of EVs used are represented by decision variables of the optimization process and, the EVs are allowed to pass through intersections several times
EVs are allowed to pass through intersections more than once
Summary
The motivation, literature review, contributions and paper organization are presented. A. MOTIVATION Vehicle route planning has been traditionally implemented for vehicles with internal combustion engines. A wellknown methodology is Dijkstra’s algorithm, which is used to determine the shortest route [1]. Internal combustion engine vehicles depend on non-renewable energy sources, which produce polluting gases, such as CO2 or NOx. Nowadays, due to environmental policies projected towards the future, it is necessary to use non-polluting energy sources. New technologies have emerged to reduce this problem. An example of that is the Electric Vehicle (EV), whose operation is characterized by zero emissions [2], [3]
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