With the development of “One Belt, One Road” initiative and free trade area, the volume of cross-border international logistics involving multiple modes of transport has surged. Meanwhile, the proportion of using integrated transportation system in domestic trunk transport has increased. Multi-modal transport (MMT) based on green transport can realize intensive utilization of transport capacity resources, and implement sustainable transport management with three bottom lines of economic, environmental and social aspects. In this paper, the carbon emission index and regional transportation infrastructure utilization index are introduced to construct a multi-objective optimization model with sustainable goals of environmental protection, cost saving and social contribution. The poly-population genetic algorithm (PPGA) is used to overcome the limitation of the traditional genetic algorithm running to the local optimum. The model proposed by this paper quantifies environmental and social indicators, balances comprehensive performance of environment, economy and society, and provides quantitative decision making support for carriers, international freight forwarder or third party logistics to carry out green MMT.
The thesis forecasts order of Cigarette Distribution Center by period, brand, district, from several dimensions. The thesis uses different models and analyses forecast results.When forecasting year's order amount, the mean accuracy of Logarithm Regression Model is highest, gets 98.45%. When forecasting month's order amount of cigarette, the thesis uses Genetic Algorithm (GA) to optimize BP neural networks, and overcomes the shortcomings that Neural Networks apt to be trapped in local optimum when searching values of weights[1]. The thesis also uses Regression Models, Grey Model and Selfadaptive Secondary Exponent Smooth Model, The mean accuracy of Linear Regression Model whose effect of forecasting is better is 96.9%.Considering the practical work period of Cigarette Distribution Center and the seasonal influences, when forecasting day's order amount, the thesis introduces PROPERTY_TAG to revise the forecast, improve the accuracy of forecast and easy to realize programming. GA Optimized BP Neural Network Model gets the expected accuracy which is 98%. The mean accuracy of Proportion Model Based-on Lunar Calendar, Regional Trend Model and ARMA Model are 90.87%, 95.61%, 96.61% respectively.When forecasting week's order amount of Cigarette, the accuracy of Accumulation of Mean Order Amount per Day Based-on PROPERTY_TAG is 94.15% and cost time is 14s.The thesis develops Order Forecast Web System of Cigarette Distribution Center using Struts framework and makes it possible to share demand information for whole tobacco supply chain.
New energy logistics vehicles have become the general trend of urban distribution development. However, in the actual operation and self-development process of domestic new energy logistics vehicles, there have been many new problems, such as the matching degree of the number of new energy logistics vehicles and charging piles is not high, and some regions can not meet the needs of vehicle charging. Therefore, it is of great significance to study the rationalization of charging facility layout and vehicle distribution path planning. After combing and summarizing the existing literature on the layout of charging facilities and vehicle distribution path planning at home and abroad, this paper proposes to consider the location of charging pile and the optimization of distribution path.
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