In addition to the great prospects for development, unmanned distribution has attracted a great deal of social attention as a new type of logistics distribution mode. The use of electric unmanned vehicles for urban logistics distribution can not only reduce enterprise costs but also achieve non-contact distribution and rapid logistics response for unmanned logistics distribution in multiple distribution centers. A multi-distribution center urban unmanned logistics distribution path optimization model is developed for minimizing total cost, taking into account constraints such as battery capacity of electric unmanned vehicles, customer time windows, simultaneous pickup and delivery, and vehicle balance between distribution centers. In this study, an improved genetic algorithm was designed with a reasonable route optimization strategy, and its effectiveness was verified through a variety of calculation examples. Our model was compared with other variant models and parameters. As a result of the analysis, it can be seen that the established model and algorithm can improve the vehicle path and save the distribution costs. This research can promote the development of urban unmanned logistics distribution mode and provide theoretical reference.
It is very important to provide material support for people in disaster area in order to save lives and reduce losses. With the development of science and technology and the popularization of 5G network, Unmanned Aerial Vehicle (UAV, or drone) will be widely used in urban logistics delivery and emergency rescue. Using trucks as a launch platform for drones, and delivering logistics in a coordinated distribution mode of trucks and drones, which is expected to further improve distribution efficiency. A mixed integer programming model with the shortest total delivery time is established. An artificial bee colony (ABC) algorithm embedded with improved mileage-saving (the Clarke-Wright or C-W) algorithm is developed to solve this model. The results show that the established model and the proposed algorithm are effective and can provide guidance for the application of drones in emergency rescue.
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