Railway Marshalling Yards Reclassifying Capability Configuration Optimization

Railway marshalling yards layout is one of the core problem of network planning, rational reclassifying capacity configuration is the key to solve the problem of marshalling yards layout. In recent years, the number of marshalling yards under ministry control has been reduced from49to40, and continues to get smaller. In this context, the key point of marshalling yards layout optimization has changed, from the location problem to how to make the maximum effectiveness of existing marshalling yards layout, makes reclassifying capacity configuration of marshalling yards is adapt to future trends. The goal is to determine which marshalling yard needs capacity expansion, and the suitable quantity of capacity expansion, which marshalling yard's loading level does not meet the requirement and is in urgent need of adjustment. As the division of labor and layout of marshalling yards is highly non-linear association, one change of marshalling yards reclassifying capacity configuration will affect the division of labor and other marshalling yard's loading degree, even the whole situation of car flow organization in the network will be modified. So the optimization procedure of marshalling yards reclassifying capacity configuration cannot take one yard to another, decision making must be from a global perspective, comprehensively demonstrate the optimized plan of marshalling yards reclassifying capacity configuration and car flow organization.Our existing marshalling yards reclassifying capacity configuration is difficult to meet the evolving transportation needs. It prevents the efficiency of transportation and the competitiveness of the industry from improving and enhancing. This issue attracts attention recently from many experts and scholars. Most of the theoretical results now available achieved marshalling yards layout optimization through the options enumeration and comparison, but it's difficult to verify whether the option is feasible and valid. With the railway development and reform, factors like transportation demand growth, the improving proportion of direct train from loading area, locomotive extended routing and so on will influence the configuration of marshalling yards reclassifying capacity. Currently this research field is lack of exploration.This dissertation based on the international research results, closely integrates the characteristics of China's railway transportation and the future direction of development, a series of optimization models and methods for solving marshalling yards reclassifying capacity configuration problems is advanced. The result provides some new ideas and means for future network planning and engineering building, and will significantly promote the railway transportation development harmonious, stable and rapid.The major research content and innovation in the dissertation presented as follows:1. Using the principle of complex network to analyzes the features of marshalling yards reclassifying capacity configuration in China. Through the result analysis of railway physical network we can known that, marshalling yards'degree are greater than average degree of whole network. Betweenness and degree distribution are positively correlated. Some of the marshalling yards have prominent vulnerability. Through the analysis of the railway transport network we known that, marshalling yards'in-strength and in-degree, out-strength and out-degree shows positive related trends, marshalling yard which has large in-strength or out-strength leans to connect with stations which have low in-strength or out-strength, In addition, railway transport network shows a local cluster structure. There are some limitations when use complex network theory to optimize reclassifying capacity configuration, thus other solutions need to be discoverd.2. For the problem of marshalling yards layout optimization, plan options are difficult to select. This dissertation designs marshalling yards'reclassifying capacity and shunting line capacity expansion decision variables, builds marshalling yards' reclassifying capacity configuration optimization model. Reclassifying capacity expansion program and marshalling yards'loading status are formed at the same time, advances adjustment suggestion for marshalling yard whose loading status is unreasonable. As the complexity of model constraints, the method of penalty function has some limitations. Combined with the characteristics of decision variables and constraints, model has been transformed. The solution strategy which confirms direct train service plan at first and then deduces car flow reclassifying plan is proposed. In order to further improve modal solution quality, the correction mechanism is designed. Based on absolute condition theory to generate marshalling destination pre-decision, describe infeasible car flow by loss costs, use sufficient condition theory to revise car flow organization program. After, parallel tabu search algorithm, mutation ant colony algorithm and adaptive particle swarm algorithms are introduced respectively to solve the model, illustrates each algorithm iterative procedure. A simplified actual railway network is put forward as example. The study shows that the adaptive particle swarm algorithm calculating time is the fastest, it can fix the results by studying from sufficient condition theory, its optimization effect is ideal. The amount of reclassifying cars will increase as car flow grows, but its growth is less then the latter. With car flow increasing, the number of direct trains will go up.The configuration of marshalling yards reclassifying capacity is closely related to direct train plan and car flow reclassifying plan, all change for one change. Final decision must be made by considering all of the options and aiming at maximizing the combined effect. Marshalling yard whose load is in low level needs to be improved by taking relevant measures, or to be abandoned. On the other hand, marshalling yard whose load is nearly saturated needs to take some means to resist the risks.3. The proportion of direct trains at loading area is considered in marshalling yards reclassifying capacity configuration optimization problem. Car flow organization decision variables are designed, clear and definite conversion relationship between car flow originated from loading station and original technology car flow, build up marshalling yards'reclassifying capacity configuration optimization model by considering proportion of direct trains. By setting a series of ladder range, the influence of different proportion of direct trains at loading area to marshalling yards reclassifying capacity configuration is simulated. Increasing the proportion of direct trains at loading area is able to reduce the demand of reclassifying and marshalling yard expansion effectively. There is a best value of loading area direct trains proportion, which makes the effectiveness of marshalling yards reclassifying capacity configuration and car flow largest. Based on above, ideal proportion of direct trains at loading area patulous model is proposed.4. The affecting factors of locomotive routing are brought into marshalling yards reclassifying capacity configuration problem. For the weaknesses that single parameter of transfer cost is not suitable for the description of locomotive routing, reclassifying cost and un-reclassifying cost were calculated separately. Then the connection between locomotive routing and un-reclassifying stations of direct trains is advanced, marshalling yards'reclassifying capacity configuration optimization model by considering locomotive extended routing is built up. Different locomotive routing is simulated by adjusting the un-reclassifying stations of direct trains, examine how locomotive extended routing affect marshalling yards'reclassifying capacity configuration. Known from the result that, under the condition of locomotive extended routing the costs of un-reclassifying and comprehensive cost will be reduced significantly. On the other hand, the labor division of marshalling yard can be optimized, leads to the demand compression of capacity expansion.5. This dissertation does preliminary exploration of marshalling yards' reclassifying capacity configuration optimization under centralized reclassifying tendency. Three different strategies:network compression, station capacity limitation and car flow organization adjustment are proposed to simulate the centralized reclassifying tendency, mathematical model is built and characteristics and drawbacks of three strategies are analyzed. Examination results reveal that the centralized reclassifying policy request marshalling yards are provided with more reclassifying capacity, shunting line capacity will be released. This problem is not only about costs, but also contains lots of social factors, so the complexity is very high.