Robust Integrated Model for Traffic Routing Optimization and Train Formation Plan with Yard Capacity Constraints and Demand Uncertainty

• Published in: International journal of ITS research Vol. 22; no. 2; pp. 407 - 415
• Main Authors: Aghaee, Shima, Alinaghian, Mehdi, Aghaee, Mohammad
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2024; Springer Nature B.V
• Subjects: Air pollution; Algorithms; Automotive Engineering; Case studies; Civil Engineering; Computer Imaging; Constraints; Demand; Design; Electrical Engineering; Engineering; Heuristic; Integer programming; Linear programming; Locomotives; Mathematical models; Optimization; Outdoor air quality; Pattern Recognition and Graphics; Performance evaluation; Rail transportation; Railroad yards and terminals; Railway networks; Robotics and Automation; Robustness (mathematics); Shipments; Trains; Transportation networks; Uncertainty; User Interfaces and Human Computer Interaction; Vision; Iran; Traffic routing; Non-linear programing; Train formation plan; Robust optimization; Rail
• ISSN: 1348-8503; 1868-8659
• DOI: 10.1007/s13177-024-00406-3

The optimal planning of shipments with optimal traffic routing and train service provision subject to capacity limits is a major problem in rail transport. These services are the subject of the Train Formation Plan (TFP) problem, which concerns choosing how to rearrange and redistribute cars at different stations or yards. Another key issue is how to choose the right routes for trains from among the variety of paths on which they can travel between two points, which is the subject of the Traffic Routing (TR) problem. Integrating these two problems can provide better train formation and routing results. Other important issues in this area include the capacity of rail yards and the possibility of uncertainty in decision parameters like demand, which needs to be considered to have a realistic model. This article presents an integrated TFP-TR model with yard capacity constraints and demand uncertainty. The problem was formulated as a non-linear model and then the robust formulation of the model was developed using Bertsimas and Sim’s method. The objective was defined as the minimization of the cost. The solution approach was validated by applying the model to an example designed according to Chinese rail transportation network.