Abstract.The flow refueling location model is adopted to describe the traffic network considering the shortest and the second shortest paths. Three objective functions of the electric vehicle charging station placement optimal model are defined to maximize the captured traffic flow, to minimize the investment cost, and to minimize the average voltage deviation. Then, the non-dominated sorting genetic algorithm-Ⅱ is used to solve the multi-objective model. With the example of the IEEE 33-node power distribution network and the 25-node traffic network, the basic characteristics of the presented model and solving method are illustrated. INSTRUCTIONSWith the growing prominence of energy and environmental issues in recent years and the development of related technologies, more attention has been paid on electric vehicles (EVs) for their advantages in saving the energy and protecting the environment. EV charging stations, which are both public service facilities and power load facilities, need to be carefully planned before constructing. The influence of not only the traffic network, but also the power grid should be considered.Many works have been done about EV charging station placement.[1] introduces planning of EV charging and charging station construction from macro point of view, including the factors affecting the layout of EV charging stations and overall principles of charging station planning.In [2],the objective is set to minimizetotal construction cost, subject to constraints of charging station coverage and convenience for drivers to charge their EVs. Literature [3] proposes a well-organized system architecture operational scheduling method for charging and discharging of EVs.Multiobjective framework aims at minimizing total operation cost and emissions.Benders decomposition technique is used to solve the proposed model.Influence of the traffic flow is included in the planning model [4], and the super-efficiency data envelopment analysis is employed to transfer multiobjective optimization into a single-objective one.In this paper, we adopt the flow refueling location mode considering shortest and second shortest paths, to describe effect of traffic network.Three objectives of the optimal EV charging station placement model is to maximize the captured traffic flow, as well as to minimize the investment cost and average voltage deviation. EV charging station placement model Model optimal objectivesThe optimal objectives we proposed in this paper are to maximize the traffic flow that charging stations capture, to minimize the investment cost and to minimize the power bus voltage deviation.

Thispaper presents aCoal Heavy Haul Transportation Assembly Scheme Problem (CHASP), in which the time consuming functions, assembly number constraints and assembly weight constraints etc are considered. The time consuming costs consist of residence time and disassembly time. The disassembly time functions are usually nonlinear functions of unit train departure directions. Then, a nonlinear 0-1 programming is formulated for the problem and solved by lingo mathematical solver. Considering the complexity of the problem, a kind ofGenetic Algorithm is proposed to solve it. Extensive computational experiments are taken on randomly generated data, the detailed results are given and the genetic algorithm is shown to be efficient.

Dynamic designs for ship propulsion shafting can be categorised as complex multi-disciplinary coupling systems. The traditional single disciplinary optimisation design method has become a bottleneck, restricting the further improvement of shafting design. In this paper, taking a complex propulsion shafting as the object, a dynamic analysis model of the propeller-shafting-hull system was established. In order to analyse the coupling effect of propeller hydrodynamics on shafting dynamics, the propeller’s hydrodynamic force in the wake flow field was calculated as the input for shafting alignment and vibration analysis. On this basis, the discipline decomposition and analysis of the subdisciplines in design of shafting dynamics were carried out. The coupling relationships between design variables in the subdisciplines were studied and the Multi-disciplinary Design Optimisation (MDO) framework of shafting dynamics was established. Finally, taking the hollowness of the shaft segments and the vertical displacement of bearings as design variables, combined with the optimal algorithm, the MDO of shafting dynamics, considering the coupling effect of the propeller-shafting-hull system, was realised. The results presented in this paper can provide a beneficial reference for improving the design quality of ship shafting.