Abstract
With the growing interest in environmental protection and congestion, electric vehicles are increasingly becoming the important transportation means. However, electric vehicles currently face several adoption barriers including high purchasing price and limited travelling range, so the fleets where electric vehicles and conventional vehicles coexist are closer to the current fleet management status. Considering the impact of charging facilities and carbon emission, this paper proposes a vehicle routing problem with a mixed fleet of conventional and electric vehicles and soft time windows. A bi-objective programming model is established to minimize total operational cost and time penalty cost. Finally, the nondominated sorting genetic algorithm II (NSGA-II) is employed to deal with this problem. Furthermore, single-objective optimization is carried out for the two objectives, respectively, and the linear weighting method is also used to solve the problem. Through the contrast of these results and the NSGA-II results, the effectiveness of the algorithm in this paper is further verified. The results indicate that two objectives are contradictory to some extent and decision-makers need a trade-off between two objectives.
Highlights
Energy and environment are hot spots of global concern
In the majority of the aforementioned works, the authors mainly focus on the single-objective models and seldom consider the carbon emissions of Electric vehicles (EVs) or soft time windows. erefore, a Mixed Fleet Vehicle Routing Problem with Soft Time Windows (MFVRPSTW) considering carbon emissions of both EVs and conventional vehicles (CVs) is proposed in this paper, and a bi-objective programming model is established to minimize the total operational cost and the time penalty cost
E nondominated sorting genetic algorithm (NSGA-II) proposed by Deb et al [23] improved the selection process of individuals by employing the density value estimation strategy, fast nondominated sorting strategy, and elite strategy, which has been widely applied in various fields. is algorithm is one of the most popular multiobjective algorithms, which ensures the value of the optimal solution and obtains the Pareto front, and reduces the complexity of the algorithm. erefore, the performance of this algorithm is remarkable in terms of running speed and convergence, and we employ it to solve the problem under investigation
Summary
Energy and environment are hot spots of global concern. Climate and environment problems caused by the greenhouse effect emerge endlessly, which greatly threaten the people’s health and daily life. Electric vehicle routing problem with time window and mixed fleet based on the fully charged policy was examined by Li et al [14]. Macrina et al [18, 19], Mouhrim et al [20], and Yu et al [21] supposed that EVs can be partially charged and further studied the mixed fleet vehicle routing problem with time windows. Erefore, a Mixed Fleet Vehicle Routing Problem with Soft Time Windows (MFVRPSTW) considering carbon emissions of both EVs and CVs is proposed in this paper, and a bi-objective programming model is established to minimize the total operational cost and the time penalty cost.
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