A reliability model for electric vehicle routing problem under charging failure risk

This research studies the electric vehicle routing problem with time windows, partial recharges, and covering locations (EVRPTW‐PR‐CL), as an extension of the electric vehicle routing problem with time windows and partial recharges (EVRPTW‐PR), where covering locations (CLs) are facilities equipped with parcel lockers (PLs) and charging stations (CSs). The presence of PLs offers customers an alternative delivery option, where they are provided incentives to collect their parcels themselves, called self‐pickup (SP) services. The objective is to seek routing plans that minimize the sum of travel costs, fixed operational costs for used EVs and CLs, and compensation paid to customers served by SP. To solve the problem, we derive a mixed‐integer programming model and design an effective variable neighborhood search (VNS) algorithm coupled with problem‐specific neighborhood operators, a dynamic programming procedure for optimal CS insertions, and a tailored set partitioning formulation (SPF) to enhance solution quality by utilizing collected routes so far. Numerical experiments are conducted on benchmark instances. VNS not only provides new best‐known EVRPTW‐PR solutions but also solves EVRPTW‐PR‐CL instances efficiently. Lastly, we present the effects of delivery options and compensation, offering insights that help decision makers design more sustainable and cost‐effective last‐mile delivery networks.

SummaryTraveling has contributed a lot to the evolution of mankind. Today, electric vehicles (EVs) are being preferred due to their greater efficiency, comfort, and environment‐friendly qualities. The EVs' contribution to future mobility is projected to rise exponentially in years to come. To make this innovative technology more successful, there is a dire need to install a sufficient number of charging stations (CSs). As the EVs are limited by their cruising range, they require multiple recharging to cover long distances (especially in the case of logistics delivery services). Thus, there is a great need to develop an efficient and cost‐effective EV route optimization approach considering multiple recharging options and time‐of‐use (ToU) energy prices. In this regard, a novel mat‐heuristic approach named (firefly with ant colony algorithm) has been proposed to solve the problem of EVRPTW (electric vehicle routing problem with time windows) incorporating detailed modeling of multiple charging flexibility (i.e., battery swapping, partial recharge, and different charging levels) and ToU energy prices. Our proposed approach aims to minimize the total cost of traveling, which is highly influenced by the cost of recharging. Ant colony algorithm (ACA) serves as the basic optimization framework in the proposed approach, while the firefly approach explores hitherto unexplored solution space and avoids local optima. The computation performance of the proposed approach is compared with existing state‐of‐the‐art similar domain approaches such as variable neighborhood search (VNS) and ant colony optimization using local search (ACO‐LS) which has average deviation of nearly 20%–25% from with optimal solution achieved by the proposed . The proposed approach yields a near‐optimal solution with a faster convergence rate (approximately 50%) compared to other existing approaches. Moreover, the multiple recharging options modeled in our proposed approach justify their significance in terms of cost‐effectiveness for most scenarios.

Because of the limitation of battery technology and charging station infrastructure, the electric vehicle has those disadvantages such as the short range of travel, the constraint of capacity, the long charging time, fewer charging stations, the range anxiety of driver et al. Therefore, the study of the electric vehicle routing problem needs to consider more limiting factors. In this paper, the electric vehicle routing problem with time windows mathematical model with minimum total cost objective function which considering the factors that include visiting charging station, partial charging, charging cost was established. The effectiveness of this model was validated with an example that extracted from the Solomon benchmark instances. The result shows that the final routing will be more realistic if we considering more characteristic factors about the electric vehicles in the EVRPTW.

An alternating direction multiplier method with variable neighborhood search for electric vehicle routing problem with time windows and battery swapping stations

Fuzzy optimization model for electric vehicle routing problem with time windows and recharging stations

Electric vehicle routing problem with single or multiple recharges

Electric vehicles (EVs) have gained considerable popularity, driven in part by an increased concern for the impact of automobile emissions on climate change. Electric vehicles (EVs) cover more than just conventional cars and trucks. They also include electric motorcycles, such as those produced by Gogoro, which serve as the primary mode of transportation for food and package delivery services in Taiwan. Consequently, the Electric Vehicle Routing Problem (EVRP) has emerged as an important variation of the Capacitated Vehicle Routing Problem (CVRP). In addition to the CVRP’s constraints, the EVRP requires vehicles to visit a charging station before the battery level is insufficient to continue service. EV battery consumption is linearly correlated to their weight. These additional constraints make the EVRP more challenging than the conventional CVRP. This study proposes an improved Harmony Search Algorithm (HSA), with performance validated by testing 24 available benchmark instances in the EVRP. This study also proposes a novel update mechanism in the improvement stage and a strategy to improve the routes with charging stations. The results show that in small and large instances, the proposed HSA improved the number of trips to the charging stations by 24% and 4.5%, respectively. These results were also verified using the Wilcoxon signed-rank significant test.

The electric on-demand bus routing problem with partial charging and nonlinear function

With the rise of the electric vehicle market share, many logistic companies have started to use electric vehicles for goods delivery. Compared to the vehicles with an internal combustion engine, electric vehicles are considered as a cleaner mode of transport that can reduce greenhouse gas emissions. As electric vehicles have a shorter driving range and have to visit charging stations to replenish their energy, the efficient routing plan is harder to achieve. In this paper, the Electric Vehicle Routing Problem with Time Windows (EVRPTW), which deals with the routing of electric vehicles for the purpose of goods delivery, is observed. Two recharge policies are considered: full recharge and partial recharge. To solve the problem, an Adaptive Large Neighborhood Search (ALNS) metaheuristic based on the ruin-recreate strategy is coupled with a new initial solution heuristic, local search, route removal, and exact procedure for optimal charging station placement. The procedure for the O(1) evaluation in EVRPTW with partial and full recharge strategies is presented. The ALNS was able to find 38 new best solutions on benchmark EVRPTW instances. The results also indicate the benefits and drawbacks of using a partial recharge strategy compared to the full recharge strategy.

This paper addresses the Electric Vehicle Routing Problem with Simultaneous Pickup and Delivery (EVRP-SPD), in which electric vehicles (EVs) simultaneously deliver goods to and pick up goods from customers. Due to the limited battery capacity of EVs, their range is shorter than that of internal combustion vehicles. In the EVRP, in addition to the depot and the customers, there are also charging stations (CS) because EVs need to be charged when their battery is empty. The problem is formulated as an integer linear model, and an efficient solution is proposed to minimize the total distance traveled. To create a feasible initial solution, Clarke and Wright’s savings algorithm is used. Several variants of variable neighborhood search are tested, and the reduced-variable neighborhood search algorithm is used to find the best solution in a reasonable time. Computer experiments are performed with benchmark instances to evaluate the effectiveness of our approach in terms of solution quality and time. The obtained results show that the proposed method can achieve efficient solutions in terms of solution quality and time in all benchmark instances.

The paper studies the Electric Vehicle Routing problem with time-window (EVRPTW) in the field of logistics maintenance optimization. We use mixed integer linear programming techniques to model the EVRPTW considering various factors encountered in practice, including the battery's capacity limit, charge station selection, and battery's charging time determination, in order to improve the availability of electric vehicle in modern logistics system. Computational experiments are carried out to verify the feasibility of the proposed model and compare it with traditional routing model, which show that our model fits better for real-life applications and can yield better schedules that improve the utilization of electric vehicles.

Partial linear recharging strategy for the electric fleet size and mix vehicle routing problem with time windows and recharging stations

An improved matheuristic for solving the electric vehicle routing problem with time windows and synchronized mobile charging/battery swapping

As CO2 emission regulations increase, fleet owners increasingly consider the adoption of Electric Vehicle (EV) fleets in their business. The conventional Vehicle Routing Problem (VRP) aims to find a set of routes to reduce operational costs. However, route planning of EVs poses different challenges than that of Internal Combustion Engine Vehicles (ICEV). The Electric Vehicle Routing Problem (E-VRP) must take into consideration EV limitations such as short driving range, high charging time, poor charging infrastructure, and battery degradation. In this work, the E-VRP is formulated as a Prognostic Decision-Making problem. It considers customer time windows, partial midtour recharging operations, non-linear charging functions, and limited Charge Station (CS) capacities. Besides, battery State of Health (SOH) policies are included in the E-VRP to prevent early degradation of EV batteries. An optimization problem is formulated with the above considerations, when each EV has a set of costumers assigned, which is solved by a Genetic Algorithm (GA) approach. This GA has been suitably designed to decide the order of customers to visit, when and how much to recharge, and when to begin the operation. A simulation study is conducted to test GA performance with fleets and networks of different sizes. Results show that E-VRP effectively enables operation of the fleet, satisfying all operational constraints.

Development of energy consumption optimization model for the electric vehicle routing problem with time windows