Electric vehicle routing problem with pickup and delivery under the partial recharging and non-linear discharging strategy

In this paper, a new variant of the electric vehicle (EV) routing problem, which considers heterogeneous EVs, partial recharge, and vehicle recycling, is investigated based on logistic companies' practical operation. A mixed integer linear programming (MILP) model is proposed to formulate the problem. For small-scale scenarios, commercial solver, e.g., CPLEX, is leveraged. For large-scale instances faced by practical applications, a hybrid metaheuristic is designed through integrating a modified Greedy Algorithm with the Variable Neighborhood Search (VNS). The proposed algorithm was tested by real-world instances from JD, an e-commerce enterprise in China. Computational results indicate that partial recharge and vehicle recycling can save costs effectively. It also shows that the number of charging stations is an important factor for the application of EVs.

Optimization of electric vehicle recharge schedule and routing problem with time windows and partial recharge: A comparative study for an urban logistics fleet

This paper presents an extension of the Electric Vehicle Routing Problem with Time Windows and Partial Recharges (EVRPTW-PR), which incorporates the use of parcel lockers as a delivery method (i.e., self-pickup method). This variant, named the electric vehicle routing problem with time windows, partial recharges, and parcel lockers (EVRPTW-PR-PL), focuses on minimizing delivery costs by employing a homogeneous fleet of electric vehicles (EVs) and providing two delivery methods for serving customers: home delivery and self-pickup methods. We derive a mathematical formulation and propose an adaptive large neighborhood search (ALNS) algorithm to address EVRPTW-PR-PL. Moreover, in ALNS, the solution representation is constructed to handle the assignment of delivery methods. The performance of our proposed ALNS algorithm is evaluated by solving EVRPTW-PR benchmark instances. Finally, the results of EVRPTW-PR-PL obtained by using the GUROBI solver and our ALNS algorithm are provided, accompanied by managerial insights on the implementation of parcel lockers.

The electric vehicle routing problem with time windows, partial recharges, and covering locations

An online decision-making strategy for routing of electric vehicle fleets

In the modern world of technology and innovation, electric vehicle (EV) integration into supply chain management has gained much interest as a feasible way to address the problems connected with freight transportation. The Electric Vehicle Routing Problem (EVRP) is carefully examined in this paper, which covers crucial aspects such as EVRP variations, optimization strategies, infrastructure development, and sustainability issues. Alongside specialized and efficient algorithms such as Ant Colony Optimization (ACO) and modified NSGA-II for multi-objective optimization, variants such as Vehicle Routing Problem with Pickup and Delivery (VRPPD), Time Windows (VRPTW), and Capacitated Routing Problem (CVRP) are inspected. To outplay issues with range restrictions, charging time, and cost, the study insists the critical roles that battery technology, effective charging techniques, and infrastructure expansion play. In order to promote green supply chain techniques, route optimization and truckload consolidation are stressed. Furthermore, new developments such as 5G connectivity and driverless EVs are recognized as revolutionary for EVRP. This work synthesizes impactful studies to discover research gaps and recommends future possibilities, such as accommodating multi-objective models and hybrid metaheuristic techniques to fulfill environmental and operational objectives in EVRP. This paper offers an intensive framework for enhancing EV routing research and practice in the context of supply chain management. The study's research requires the inclusion of the inevitability of transforming more conventional VRP variants to their electric equivalents by adding charging station adaptability to existing EVRP models and creating accurate algorithms for optimal routing solutions. The paper also identifies a shortage of research on multi-objective models that take resilience, social, and green elements into priority, as well as hybrid and advanced metaheuristic algorithms. Hence, new models and algorithms are needed to handle the issues posed by emerging technologies namely 5G connection and driverless cars. Furthermore, governments and the business sector are not working together to establish comprehensive charging infrastructure for effective EV routing. The results show how crucial Electric Vehicle Routing Problems (EVRP) are becoming to supply chain management, signifying the necessity of refined optimization techniques, reliable charging infrastructure, and environmentally friendly procedures. The study is to fill in knowledge gaps and improve optimization models for electric vehicle routing that is both operationally and environmentally efficient with refined performance.

With the development of green logistics and the promotion of new energy vehicle development policies domestically and abroad, electric vehicles have been put into logistics and distribution as an alternative to traditional fuel vehicles. The Electric Vehicle Routing Problem (EVRP) has attracted widespread attention from the academic community. This paper comprehensively examines the latest research progress on electric vehicle routing models and solution algorithms in logistics and distribution. Firstly briefly introduces EVRP models considering battery losses; secondly, based on the composition of the EVRP objective function and constraints, EVRP models are classified into four types: EVRP considering load and battery life constraints, EVRP with a time window and considering charging strategies, the study of vehicle routing problems for hybrid fleets, and EVRP combined with charging/swapping station location. Then, briefly introduce exact algorithms, traditional heuristics, meta-heuristics, and hybrid algorithms for solving EVRP models. Moreover, it analyzes the main meta-heuristics that are more widely used. Finally, this review points out the development trend of EVRP theoretical methods.

Over the past decade, electric vehicles (EVs) have been considered in a growing number of models and methods for vehicle routing problems (VRPs). This study presents a comprehensive survey of EV routing problems and their many variants. We only consider the problems in which each vehicle may visit multiple vertices and be recharged during the trip. The related literature can be roughly divided into nine classes: Electric traveling salesman problem, green VRP, electric VRP, mixed electric VRP, electric location routing problem, hybrid electric VRP, electric dial-a-ride problem, electric two-echelon VRP, and electric pickup and delivery problem. For each of these nine classes, we focus on reviewing the settings of problem variants and the algorithms used to obtain their solutions.

Plug-in hybrid electric refuse vehicle routing problem for waste collection

Partial recharge strategies for the electric vehicle routing problem with time windows

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.

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

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

Environmental concerns have called for several measures to be taken within the logistics and transportation fields. Among these measures is the adoption of electric vehicles instead of diesel-operated vehicles for personal and commercial-delivery use. The optimized routing of electric vehicles for the commercial delivery of products is the focus of this paper. We study the effect of several practical challenges that are faced when routing electric vehicles. Electric vehicle routing faces the additional challenge of the potential need for recharging while en route, leading to more travel time, and hence cost. Therefore, in this work, we address the issue of electric vehicle routing problem, allowing for partial recharging while en route. In addition, the practical mandate of the time windows set by customers is also considered, where electric vehicle routing problems with soft time windows are studied. Real-life experience shows that the delivery of customers’ demands might be uncertain. In addition, real-time traffic conditions are usually uncertain due to congestion. Therefore, in this work, uncertainties in customers’ demands and traffic conditions are modeled and solved using fuzzy methods. The problems of fuzzy real-time, fuzzy demand, and electric vehicle routing problems with soft time windows are addressed. A mixed-integer programming mathematical model to represent the problem is developed. A novel two-phase solution approach is proposed to solve the problem. In phase I, the classical genetic algorithm (GA) is utilized to obtain an optimum/near-optimum solution for the fuzzy demand electric vehicle routing problem with soft time windows (FD-EVRPSTW). In phase II, a novel fuzzy real-time-adaptive optimizer (FRTAO) is developed to overcome the challenges of recharging and real-time traffic conditions facing FD-EVRPSTW. The proposed solution approach is tested on several modified benchmark instances, and the results show the significance of recharging and congestion challenges for routing costs. In addition, the results show the efficiency of the proposed two-phase approach in overcoming the challenges and reducing the total costs.

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