Goods Delivery with Electric Vehicles: Electric Vehicle Routing Optimization with Time Windows and Partial or Full Recharge

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.

Download Full-text

A large neighborhood search-based matheuristic for the load-dependent electric vehicle routing problem with time windows

Annals of Operations Research ◽

10.1007/s10479-021-04320-9 ◽

2021 ◽

Author(s):

Sina Rastani ◽

Bülent Çatay

Keyword(s):

Vehicle Routing ◽

Electric Vehicle ◽

Vehicle Routing Problem ◽

Time Windows ◽

Neighborhood Search ◽

Large Neighborhood Search ◽

Routing Problem ◽

Large Neighborhood

Download Full-text

A Variable Neighborhood Search Branching for the Electric Vehicle Routing Problem with Time Windows

Electronic Notes in Discrete Mathematics ◽

10.1016/j.endm.2014.11.029 ◽

2015 ◽

Vol 47 ◽

pp. 221-228 ◽

Cited By ~ 66

Author(s):

Maurizio Bruglieri ◽

Ferdinando Pezzella ◽

Ornella Pisacane ◽

Stefano Suraci

Keyword(s):

Vehicle Routing ◽

Electric Vehicle ◽

Vehicle Routing Problem ◽

Variable Neighborhood Search ◽

Time Windows ◽

Neighborhood Search ◽

Routing Problem

Download Full-text

Evolutionary Algorithm for the Electric Vehicle Routing Problem with Battery Degradation and Capacitated Charging Stations

Annual Conference of the PHM Society ◽

10.36001/phmconf.2020.v12i1.1281 ◽

2020 ◽

Vol 12 (1) ◽

pp. 9

Author(s):

Juan Pablo Futalef ◽

Diego Muñoz-Carpintero ◽

Heraldo Rozas ◽

Marcos Orchard

Keyword(s):

Vehicle Routing ◽

Electric Vehicle ◽

Vehicle Routing Problem ◽

Combustion Engine ◽

Time Windows ◽

Route Planning ◽

Routing Problem ◽

Charging Infrastructure ◽

Conventional Vehicle ◽

Battery Degradation

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.

Download Full-text

The Electric Vehicle Routing Problem with Time Windows under Travel Time Uncertainty

Journal of Physics Conference Series ◽

10.1088/1742-6596/2095/1/012032 ◽

2021 ◽

Vol 2095 (1) ◽

pp. 012032

Author(s):

Dan Wang ◽

Hong Zhou

Keyword(s):

Travel Time ◽

Vehicle Routing ◽

Electric Vehicle ◽

Electric Vehicles ◽

Vehicle Routing Problem ◽

Search Algorithm ◽

Time Windows ◽

Routing Problem ◽

Time Uncertainty ◽

Travel Time Uncertainty

Abstract Due to environmental friendliness, electric vehicles have become more and more popular nowadays in the transportation system. For many express companies, it is more and more important to meet the predetermined time window of customers. The uncertainty in travel times often causes uncertain energy consumption and uncertain recharging time, thus electric vehicles may miss the time windows of customers. Therefore, this paper addresses the electric vehicle routing problem with time windows under travel time uncertainty, which aims to determine the optimal delivery strategy under travel time uncertainty. To solve this problem, a robust optimization model is built based on the route-dependent uncertainty sets. However, considering the complexity of the problem, the robust model can only solve few instances including the small number of customers. Thus, a hybrid metaheuristic consisting of the adaptive large neighborhood search algorithm and the local search algorithm is proposed. The results show that the algorithm can obtain the optimal solution for the small-sized instances and the large-sized instances.

Download Full-text