| The logistics industry has now become a pillar industry in our country.As a problem that must be faced in logistics scenarios,vehicle routing has always been focused on by practitioners in the logistics industry.The classic vehicle routing problem focuses on how to reduce the number of vehicles used,how to reduce the total distance traveled,and other issues that are directly associated with economic costs.But the cost of efficient logistics is huge damage to the environment.The green vehicle routing problem is a new type of problem that is different from the classic vehicle routing problem in terms of optimiza-tion goals.It studies pollution problems caused by logistics and transportation.The goal of optimization is to reduce the negative impact on the environment,including reducing carbon emissions and the use of new energy to replace traditional energy to reduce the consumption of traditional energy.In this paper,these two aspects of optimizing green vehicle routing problems with time windows are studied separately.Fuel vehicle is one of the main bodies of transportation used in the problem of Green-Pickup and Delivery Routing Problem with Time Windows.In the scenario of this prob-lem,the vehicle needs to travel between the customers to meet the requirements of com-pleting the delivery task within the specified time of each order.The customers corre-sponding to each order can only be visited once.In the process of transportation,fuel vehicles will inevitably produce a large number of greenhouse gases during the driving process,thereby destroying the atmosphere.To reduce the carbon emissions caused by vehicle transportation,this paper proposes a Distance-Based Adaptive Large Neighbor-hood Search algorithm.The effectiveness of this algorithm in reducing carbon is verified through comparative experiments,and it is also superior to the comparison algorithm in terms of economic benefits.This paper also studies the suitable application scenarios of the model and the proposed algorithm.On the same data set and the same algorithm,the experimental results obtained with different problem models show that when the cus-tomers are in a clustered distribution state,the carbon reduction advantage of the algorithm is more obvious.Another way to reduce carbon emissions is to use new energy represented by electric energy as the main energy source for vehicles.Open Vehicle Routing Problems are com-mon in scenarios such as logistics outsourcing and express delivery.In these scenarios,the vehicle does not need to return to the depot after completing the delivery task but needs to complete all the delivery tasks within the maximum route time.With the development of the electric vehicle industry and the country’s strong support for new energy vehicles,trucks with electric energy as the main energy source will take on more and more work in road transportation.However,there is no research on the Open Vehicle Routing Problem with electric vehicles as the main body of transportation.In response to this research gap and the needs of market applications,this paper establishes a corresponding mathematical model and proposes an Improved Combined Algorithm of Variable Neighborhood Search and Simulated Annealing to solve this problem.The optimization goal of this problem is the shortest routing duration.In the comparative experiment,the proposed algorithm can obtain the shortest total distance traveled in most of the calculation examples,which reflects the optimization ability of the algorithm.For different types of charging stations,the experiment generates two sets of data according to the distribution of charging station type and conducts experiments respectively.The experimental results show that the ran-domly distributed type of charging stations have a positive effect on vehicle routing in the complicated scene.At the same time,it was found in the test experiment with the subjec-tive emotions of the driver that the algorithm is more effective in optimizing the vehicle routing in complex scenes. |
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