# SVD Enhanced Metaheuristic for Plug-In Hybrid Electric Vehicle Routing Problem
## 2 Literature Review  
The Vehicle Routing Problem (VRP) was first introduced in 1959. Since then, many variants of the VRP problem have been studied. Recently, the environmental impacts of logistics transportation have grabbed more attention. Green logistics has therefore become a hot research topic. It was first proposed by [PERSON] et al. [12] and [PERSON] et al. [13], and it takes environmental problems into combinatorial optimization problems. Indeed, green logistics has attracted increasing research attention. For example, the GVRP was proposed by [PERSON] and [PERSON] et al. [5], and focuses on reducing emissions and eliminating environmental impacts. In their work, they considered the limited fuel capacity of vehicles and the possibility of refueling at fuel stations along the way. In recent years, there have been many studies on the variants of GVRP and there are some studies combining carbon emissions or energy consumption with different constraints and optimization objectives. For example, [PERSON] et al. [14] proposed the Dynamic Green Vehicle Routing Problem (DGVRP), which combines the Dynamic Vehicle Routing Problem (DVRP) with GVRP. [PERSON] et al. [15] took into account the distribution efficiency and carbon emissions and developed a cargo flow modeling method to analyze logistics planning. [PERSON] et al. [16] conducted a study aimed at minimization of both traveling distances and pollutant emissions. For GVRP, there are two kinds of solutions based on exact and heuristic algorithms. [PERSON] et al. [17] proposed a Lagrangian Relaxation-Based Solution for GVRP. [PERSON] et al. [18] proposed a new tabu search named the RS-TS algorithm to solve the Vehicle Routing Problem with fuel consumption and carbon emissions. [PERSON] et al. [19] used an adaptive large neighborhood search algorithm (ALNS) to minimize driving time and fuel consumption, respectively. Lately, many works have focused on the path planning problem of new energy vehicles. The path planning problem for electric vehicles is called the electric vehicle routing problem (EVRP). [PERSON] et al. [20] proposed a location routing method for charging stations, which considered both the path of electric vehicles and the location of charging stations. On the basis of EVRP, [PERSON] et al. [21] considered the impact of an electric vehicle's driving speed and cargo load on energy consumption. They adopted a hybrid genetic algorithm to solve the problem. For EVRP, latter works also considered time window constraints. [PERSON] and [PERSON] [22] investigated the Electric Traveling Salesman Problem with Time Windows. [PERSON] and [PERSON] [23] discussed the electric vehicle routing problem with time-windows and partial recharging (EVRP-TWPR). In recent works on EVRP, the objectives have been to reduce energy consumption, cost, distance and so on. In terms of constraints, the layout of charging stations, partial charging, time window and variable speed are considered. For example, [PERSON] et al. [24] applied the ant colony algorithm (ACO) for EVRP which estimated whether the electric vehicle has a charging station in its working range. [PERSON] et al. [25] introduced a hybrid heuristic for EVRP with time windows and recharging stations. [PERSON] et al. [26] provided a comprehensive survey of HEVs' source combination, models, energy management systems and so forth. [PERSON] et al. [27] discussed the plug-in electric vehicles (PEVs) charging selection problem. Plug-in hybrid vehicles are regarded as one of the most environment-friendly solutions for logistic transportation. Actually, there are more related works that try to provide a solution for HVRP. The vehicle routing problem for new hybrid energy vehicles is termed HVRP, which was proposed by [PERSON] [6]. The author defined HVRP as an extension of GVRP, in which vehicles can use both electricity and fuel, the unitary travel cost is much lower for distances covered in electric mode, a mixed integer linear programming formulation and a large neighborhood search based on matheuristic are proposed.