Research On High Speed Railway Line Planning Based On Mixed Set Programming

Optimization of line planning is among the core problems in railway transportation. It reflects the operation strategy and service of railway transportation. Mixed set programming, when applied to optimize line planning, can target the logical constraints and non-linear constraints, construct a more compact model, and design a more effective algorithm to tackle complex and large-scale engineering problem. It can present a new perspective and support to the optimization of line planning.After analyzing a large amount of research from home and abroad, the model and algorithm to optimize line planning have been constructed by using mixed set programming. The thesis includes the following aspects:1. Introduction to and analysis on the theories and features of constraint programming, mixed set programming and nature constraint language NCL; the definition of line planning, its components, influencing factors and classical models; and how mixed set programming can optimize line planning in the context of China’s high speed railway transportation.2. Line planning optimizing model based on mixed set programming. This part starts with a description of the business, and then follows constraint analysis, target analysis, and data modeling. Second, the ORDER-SHIFT-TOUR system is established. Based on the system, NCL language is used to analyze passenger demand and potential lines from the perspective of train matching and the constraints of business, and to identify the logical constraints and numerical constraints. Then the mixed set programming model is constructed which integrated passenger flow distribution and train transfer.3. The model established previously and the business rules of line planning are analyzed. Stable and effective algorithm is designed based on a large amount of benchmark, and a case study is conducted on the line planning of Beijing-Shanghai High Speed Railway. The thesis demonstrates the feasibility and effectiveness of using mixed set programming to optimize line planning.