Green vehicle routing problem: A state-of-the-art review

The electric vehicle routing problem and its variations: A literature review

This paper studies green vehicle routing problems of cold chain logistics with the consideration of the full set of greenhouse gas (GHG) emissions and an optimization model of green vehicle routing for cold chain logistics (with an acronym of GVRPCCL) is developed. The purpose of this paper is to minimize the total costs, which include vehicle operating cost, quality loss cost, product freshness cost, penalty cost, energy cost and GHG emissions cost. In addition, this research also investigates the effect of changing the vehicle maximum load in relation to cost and GHG emissions.,This study develops a mathematical optimization model, considering the total cost and GHG emission. The standard particle swarm optimization and modified particle swarm optimization (MPSO), based on an intelligent optimization algorithm, are applied in this study to solve the routing problem of a real case.,The results of this study show the extend of the proposed MPSO performing better in achieving green-focussed vehicle routing and that considering the full set of GHG costs in the objective functions will reduce the total costs and environmental-diminishing emissions of GHG through the comparative analysis. The research outputs also evaluated the effect of different enterprises’ conditions (e.g. customers’ locations and demand patterns) for better distribution routes planning.,There are some limitations in the proposed model. This study assumes that the vehicle is at a constant speed and it does not consider uncertainties, such as weather conditions and road conditions.,Prior studies, particularly in green cold chain logistics vehicle routing problem, are fairly limited. The prior works revolved around GHG emissions problem have not considered methane and nitrous oxides. This study takes into account the characteristics of cold chain logistics and the full set of GHGs.

A green vehicle routing model based on modified particle swarm optimization for cold chain logistics

We propose a three-index formulation for E-VRP with Non-Linear charging and Load-Dependent discharging (E-VRP-NL-LD), and an Adaptive Large Neighborhood Search (ALNS) algorithm to solve the E-VRP-NL- LD and E-VRP-NL-LD with Capacitated Charging Stations (E-VRP-NL-LD-CCS). Existing implementations of EVRP duplicate charging station nodes which enables the modelling of EVRP using extended VRP formulations. Two limitations of such an approach are: (i) the number of such duplications is not known a priori, and (ii) the size of the problem increases. In our formulation, we allow multiple visits to a charging station without duplicating nodes. We propose five new operators for ALNS which are tested on 120 instances each of E-VRP-NL and E-VRP-NL-LD, and 80 instances of E-VRP-NL-LD-CCS. Results show that our ALNS outperforms the existing algorithms improving the solution in 63% of the instances and matching the best known solution in 31% of the instances. Results also show that considering load-dependent discharge is critical to optimally solve E-VRP optimally.

Electric vehicle routing problem with non-linear charging and load-dependent discharging

Sustainable last mile delivery on e-commerce market in cities from the perspective of various stakeholders. Literature review

Incorporating driving cycle based fuel consumption estimation in green vehicle routing problems

Electric vehicles are gaining popularity day-by-day aided by growing pollution concerns with fossil fuel vehicles. Many logistics companies have already started testing electric vehicles for deliveries in cities. However, electric vehicles have issues such as range anxiety and long recharge times. These issues have to be considered in routing electric vehicles to avoid inefficient routes. One of the important factors that affects the amount of battery consumed is load carried by the vehicle. Considering loads will significantly affect the routes determined in the electric vehicle routing problem (EVRP). Most previous studies solved EVRP with distance minimization as the objective. We have considered load of vehicle in the power estimation function to calculate the energy requirement. An adaptive large neighborhood search (ALNS) with special operators particular to this problem structure is presented. ALNS was tested on 56 benchmark instances and it found better solutions for 14 instances and for 15 instances the solutions matched the best-known solution.

An Adaptive Large Neighborhood Search Approach for Electric Vehicle Routing with Load-Dependent Energy Consumption

We formulate the two-echelon routing problem considering multiple depots and heterogeneous fleets. Our study (a) presents a Mixed Integer Linear Programming (MILP) formulation with load-dependent fuel minimization objective, (b) uses driving cycles to represent speed variations along a path, (c) allows the vehicles to return to any depot/satellite, and (d) conserves the total number of vehicles at each depot/satellite. We call the problem a Multi-Depot Two-Echelon Fuel Minimizing Routing Problem (MD2E-FMRP). Prior studies assumed there is a fixed number of vehicles available at each satellite/depot, whereas we allow different number of vehicles of each vehicle type at each satellite and depot. Our formulation relaxes several unrealistic assumptions in existing two-echelon formulations and hence has greater practical application. Despite the relaxation of constraints, the running time of our model is comparable to existing formulations. Gurobi optimizer is used to find a better upper bound for up to 56 node instances within a given time limit of 10,000s. We also propose an Adaptive Large Neighborhood Search (ALNS) based heuristic solution technique that outperformed Gurobi in all the tested instances of MD2E-FMRP. We observe an average saving of 13.11% in fuel consumption by minimizing fuel consumed instead of minimizing distance. In general, adapting heterogeneous fleets results in fuel savings and consequently lower emissions compared to using a homogeneous fleet.

Multi-depot Two-Echelon Fuel Minimizing Routing Problem with Heterogeneous Fleets: Model and Heuristic

Estimating emissions savings from alternative vehicular technologies is essential to streamline urban freight transport policy. We estimate freight emissions in the city of Chennai, India using real-world emission factors obtained from on-board emission measurement systems and trip characteristics collected through an establishment survey. India recently enforced Bharat Stage VI (BS-VI) emission standards and both the central and multiple state governments have introduced battery electric vehicle (BEV) policies. To understand potential benefits, we estimate the emission savings with BS-VI vehicles and BEVs for freight delivery. The BS-VI vehicles reduce tailpipe CO emissions by 20.3%, and HC+NOx emissions by 74.5%. The GHG emission saving in CO2 equivalents with BEVs is 50.3%, which increase with a greener energy mix, improved transmission and distribution efficiency, cleaner battery production, and more lifetime vehicle kilometres. BS-VI adoption can deliver significant emission reduction until BEVs and charging infrastructure scale up. 

Surendra Reddy Kancharla

Papers

Electric vehicle routing problem with non-linear charging and load-dependent discharging

Incorporating driving cycle based fuel consumption estimation in green vehicle routing problems

An Adaptive Large Neighborhood Search Approach for Electric Vehicle Routing with Load-Dependent Energy Consumption

Multi-depot Two-Echelon Fuel Minimizing Routing Problem with Heterogeneous Fleets: Model and Heuristic

Complete LCA of battery electric and conventional fuel vehicles for freight trips