Heterogeneous fleet electric vehicle routing optimization for logistic distribution with time windows and simultaneous pick-up and delivery service

Li, Lin; Li, Tong; Wang, Kai; Gao, Shuai; Chen, Zhiyuan; Wang, Li

doi:10.1109/icsssm.2019.8887631

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…The authors compared the performance of their algorithm with the large adaptive neighborhood search algorithm proposed by Goeke and Schneider [9] and showed that the ant colony optimization algorithm could provide better results for the routing of electric vehicles in computational times. Li et al [17] introduced a mixed-integer mathematical programming model considering the heterogeneous electrical fleet with time windows and simultaneous collection and delivery, aiming to minimize distance. In their study, energy consumption is proportional to the travel time, and a load of electric vehicles and the variable neighborhood search algorithm are used to solve the model.…”

Section: Related Workmentioning

confidence: 99%

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A heterogeneous electric taxi fleet routing problem with recharging stations to maximize the company’s profit

et al. 2023

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During the past years, many kinds of research have been done in order to reduce the cost of transportation by using different models of the vehicle routing problem. The increase in the amount of pollution caused by vehicles and environmental concerns about the emission of greenhouse gases has led to the use of green vehicles such as electric vehicles in the urban transport fleet. The main challenge in using electric vehicles with limited battery capacity is their long recharging time. For this purpose, several recharging stations are considered in the transportation network so that if the battery needs to be recharged, the electric vehicle can recharge and complete its journey. On the other hand, due to the limited amount of the electric vehicle's energy, the fuel consumption of this fleet is highly dependent on their load, and it is necessary to consider their load in the planning. In this article, the problem of routing electric taxis is presented considering the economic and environmental aspects of implementing electric taxis for city services. Despite other studies that have only focused on reducing energy consumption or minimizing distance traveled by electric vehicles, for the first time, the problem of urban electric taxi routing has been modeled by considering different types of electric taxis with the aim of achieving the maximum profit of this business. The use of a heterogeneous fleet in this study leads to wider coverage of different types of demand. Therefore, a mathematical programming model is presented to formulate the problem. Then, several problem examples are designed and solved for validation purposes, and the simulated annealing algorithm (SA) will be introduced and used to solve large-scale problems.

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Section: Related Workmentioning

confidence: 99%

“…Controls the battery level when moving from Depot nodes. Constraint (17) ensure that the weight is reduced after the customer arrives. Constraint (18) limits the maximum load carried by an electric taxi k and force if 𝑤 𝑘 𝑖𝑗 = 0 then 𝑥 𝑘 𝑖𝑗 = 0.…”

Section: Notations and Model Formulationmentioning

confidence: 99%