遺傳算法在多式聯(lián)運(yùn)路徑優(yōu)化的應(yīng)用
[Abstract]:Multimodal transport in our country refers to the process of connecting two or more modes of transport through transshipment, so as to deliver the goods from the starting point to the destination. By using multimodal transport, the advantages and disadvantages of various modes of transport can be fully utilized, and the seamless connection between transport time and space can be realized. The route optimization of multimodal transport should not only consider time and cost, but also consider the connection of transportation mode, the risk and loss of transshipment, the quality of transportation, the level of service and so on. These indexes of different modes of transport are different, so multimodal transport is a very complex system. In this paper, the model of multimodal transport with time window is studied, and the influence of transportation frequency on transport time is introduced. The optimization model of multimodal transport with transportation frequency as parameter is established. A genetic algorithm based on Pareto optimization is used to solve multiple objectives of the model simultaneously. The research contents are as follows: (1) analyze the development of multimodal transport in China and domestic and foreign research status, summarize and sort out the relevant literature, the research direction is divided into the operation management research and the network cargo flow planning research. The network cargo flow planning includes the combination of route optimization and transportation mode. This paper studies and discusses the network cargo flow planning. Through the research to find out the research direction and the strategy that should be adopted in the multimodal transport according to the situation of our country. (2) taking the lowest cost, the minimum time and the minimum carbon emission as the goal to establish the multimodal transport model, the model considers the time factor, establishes the time window, Punish both early and late arrival at the target node. Then considering the limitation of each node's transportation mode, the alternative set is added as the constraint condition, and the waiting time is set according to the shift. (3) the concrete operation steps of the model are set by genetic algorithm based on Pareto. The data are brought in, solved step by step, and compared with the model using only time window. By modifying the departure time of the goods in the model, the effect of the model on the result is studied.
【學(xué)位授予單位】:杭州電子科技大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2017
【分類號】:TP18;F512.4
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