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Nowadays, all the countries are devoting themselves to replace fossil fuels with gasoline in order to protect the environment. This article analyzes how the market share of electric cars will change during the conversion of petrol and diesel vehicles to electric vehicles. We construct two models. The first destination charging station network model targets at how to distribute charging stations. We use network analysis to convert this problem into location model. Instead of focusing on the convenience of users, this model takes the biggest service areas into consideration with the least stations. The other super charging station network model is based on Model P-Median. It aims at charging the most electric cars within limited routes. When the electric cars cant be equally distributed, we can use this model to predict the distribution of the super charging stations. Not only charging stations, this model can also be widely used in other location problems, such as infrastructure construction, new energy facility location, location of large enterprise branches and so on, with strengths that it is forward-looking, effective, reliable and has a wide range of applications. [ABSTRACT FROM AUTHOR]

In this paper a cooperative location model based on Multiple-Input Multiple-Out-put (MIMO) communication with double circular antenna array is proposed for the needs of emergency environment, in which improved Multiple signal classification (MUSIC) algorithm for high speed scanning beam is used combined with technologies of Space Division Multiple Access (SDMA), Time Division Multiple Access (TDMA) and pulse radar location, and also methods of estimating source Direction Of Arrival (DOA) by accumulating detection, testing distance of echo wave-path difference, detecting location of clock synchronization code beacon are used as cooperative communication location and status awareness. The simulation results show that better precision and shorter time for cooperative location can be achieved with proposed model and improved MUSIC algorithm. [ABSTRACT FROM PUBLISHER]

The multi-layer urban logistics facilities location is not only influenced by service radius, but also be readily influenced by the capacity of the facilities and the relationship of different demand points. Therefore, considering the constraints of capacity and correlation, the multi-level urban logistics infrastructures location model with constraints of capacity and correlation is developed based on some modifications and extension for the classical model with constraints of service radius. The solving strategy including genetic algorithm (GA) and Partical Swarm Optimization (PSO) is proposed to obtain the location-distribution solutions. Finally, a numerical research is done, and the results are compared with the situation of classical model with constraints of service radius which show the effectiveness of the proposed method and explain the stability and dynamics of multi-layer urban logistics facilities location, these provide theory support of logistics location for decision makers. [ABSTRACT FROM PUBLISHER]