Abstract
Electric vehicles (EVs) have been widely used in urban cold chain logistic distribution and transportation of fresh products. In this paper, an electric vehicle routing problem (EVRP) model under time-varying traffic conditions is designed for planning the itinerary for fresh products in the urban cold chain. The object of the EVRP model is to minimize the total cost of logistic distribution that includes economic cost and fresh value loss cost. To reflect the real situation, the EVRP model considers several influencing factors, including time-varying road network traffic, road type, client’s time-window requirement, freshness of fresh products, and en route queuing for charging. Furthermore, to address the EVRP, an improved adaptive ant colony algorithm is designed. Simulation test results show that the proposed method can allow EVs to effectively avoid traffic congestion during the distribution process, reduce the total distribution cost, and improve the performance of the cold chain logistic distribution process for fresh products.
Highlights
With the increase of greenhouse effect, clean energy is the energy of the future for traffic development
Data show that the number of pure electric vehicles in China rose to 3.1 million in 2019, which is the highest number of EVs compared with the EV numbers for a few years ago, accounting for about 40% of the global EV ownership
With the increasing popularization of EVs, how to scientifically dispatch EVs and optimize logistic distribution routes under the constraints of EV technology to achieve economic and environmental protection goals has continually been a focus of researchers
Summary
With the increase of greenhouse effect, clean energy is the energy of the future for traffic development. In view of the problems of EVs (i.e., low mileage on a single charge and a lack of charging facilities), they studied an EVRP with time windows that accounted for charging strategies They established a mixed integer model with the minimization of the travel distance as an objective and solved it using an improved saving algorithm and a clustering algorithm. Chen et al [32] studied the optimization of the transportation routing problem for fresh food by an improved ant colony algorithm based on tabu search. Fang and Ai [34] studied the hybrid ant colony algorithm to solve the cold chain logistic distribution optimization model. (2) An improved adaptive ant colony algorithm is designed to solve the distribution optimization model with time windows for EVs in cold chain logistics for fresh products.
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