The evolutionary game of co-opetition relationship between regional logistics nodes is studied in this paper. A replicator dynamic model is built to obtain the Evolutionary Stable Strategy (ESS) of the game. Then, according to the status and level of logistics nodes, the symmetric and asymmetric game models based on evolutionary game theory are proposed, respectively. The former is used to analyze the changing trends of co-opetition relationship between logistics nodes from a same layer of a logistics network. While the latter can be used to deal with the relationship forecasting problems between logistics nodes from different layers. The result of the case study reveals that the proposed models have good practicability and accuracy in dealing with the relationship forecasting problems within a regional logistics network.
In many cities, large trucks cant be used to delivery in the urban district, but in the suburbs all kinds of vehicles can be used. To solve the problem, this paper establishes the optimization model and designs a solving method using a genetic algorithm (GA), taking Zigong tobacco distribution center for example. Comparing with the data calculated by empirical distribution route, the results show that the total distance declines 155.47 km, the number of different type vehicles reduces 1-2 and the average load rate increased by 68.3% to 85.1%.
Firstly, the consequence of the accident was divided into several ranks. Then we can get the risk fund by the fuzzy risk analysis. Secondly, the stochastic number of every route was produced by the computer, and then the risk of every section can be got. Thirdly, the shortest route theory can be used to get the minimum risk routes. The rationality of the model and the feasibility of the algorithm are proved by the computation and analysis of the example.
In order to optimize the railway freight transport network, integrate the limited transport resources and overcome the current problems existing in the traditional transport organization, in this study, we propose a three-layer railway freight transport network system, analyze its hierarchical structure and describe the respective function orientation of the railway freight stations in different layers. Then we design a BP neural network model with adaptive learning algorithm and momentum BP algorithm to classify the railway freight stations into three layers. Finally, an empirical case study is presented to test the feasibility of the BP neural network. The simulation result indicates that the BP neural network model can classify the railway freight stations into three layers under relatively high training accuracy.
Logistics network design problem has an important position in the railway logistics development, it has aroused great concern both in railway transportation and logistics research fields. This paper proposes a method for railway logistics network design problem based on artificial neural network model. In the logistics network design method, various influencing factors of railway logistics network have been considered. An evaluation index system of a railway transportation enterprise is set up. Self-Organizing Map neural network algorithm has been used for the creation of logistics network nodes initial set. And the layers division of the logistics network has been determined with the help of BP neural network model. Subsequently an empirical study of a railway logistics enterprise is given to certificate the feasibility and accuracy of this railway logistics network design method.
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