Ant colony optimization for the real-time train routing selection problem. (March 2016)

• Record Type: Journal Article
• Authors: Samà, Marcella; Pellegrini, Paola; D'Ariano, Andrea; Rodriguez, Joaquin; Pacciarelli, Dario
• Is Part Of: Transportation research. Volume 85(2016)
• Page Start: 89
• Page End: 108
• Publication Date: 2016-03
• Subjects: Real-time railway traffic management -- Train scheduling and routing -- Meta-heuristics; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2016.01.005
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: We propose a methodology for real-time near-optimal train rescheduling and rerouting. We study the problem of selecting a subset of routing alternatives for each train. We formulate the routing selection problem as a linear programming model. We solve the routing selection problem via an ant colony optimization algorithm. Our methodology improves a state-of-the-art approach remarkably. Abstract: This paper deals with the real-time problem of scheduling and routing trains in a railway network. In the related literature, this problem is usually solved starting from a subset of routing alternatives and computing the near-optimal solution of the simplified routing problem. We study how to select the best subset of routing alternatives for each train among all possible alternatives. The real-time train routing selection problem is formulated as an integer linear programming formulation and solved via an algorithm inspired by the ant colonies' behavior. The real-time railway traffic management problem takes as input the best subset of routing alternatives and is solved as a mixed-integer linear program. The proposed methodology is tested on two practical case studies of the French railway infrastructure: the Lille terminal station area and the Rouen line. The computational experiments are based on several practical disturbed scenarios. Our methodology allows the improvement of the state of the art in terms of the minimization of train consecutive delays. The improvement is around 22% for the Rouen instances and around 56% for the Lille instances.