On-line timetable re-scheduling in regional train services

Adenso‐Díaz, Belarmino; Gonzalez, Monica; González-Torre, Pilar L.

doi:10.1016/s0191-2615(98)00041-1

Cited by 112 publications

(40 citation statements)

References 10 publications

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“…A similar problem is the focus of [1]. The MIP model presented is considered to be too complicated to solve in real-time, and the authors adopt a more practical heuristic approach.…”

Section: Literature Reviewmentioning

confidence: 99%

A set packing inspired method for real-time junction train routing

Lusby

Larsen

Ehrgott

et al. 2013

Computers & Operations Research

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Efficiently coordinating the often large number of interdependent, timetabled train movements on a railway junction, while satisfying a number of operational requirements, is one of the most important problems faced by a railway company. The most critical variant of the problem arises on a daily basis at major railway junctions where disruptions to rail traffic make the planned schedule/routing infeasible and rolling stock planners are forced to reschedule/re-route trains in order to recover feasibility. The dynamic nature of the problem means that good solutions must be obtained quickly. In this paper we describe a set packing inspired formulation of this problem and develop a branch-and-price based solution approach. A real life test instance arising in Germany and supplied by the major German railway company, Deutsche Bahn, indicates the efficiency of the proposed approach by confirming that practical problems can be solved to within a few percent of optimality in reasonable time.

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“…A similar problem is the focus of [1]. The MIP model presented is considered to be too complicated to solve in real-time, and the authors adopt a more practical heuristic approach.…”

Section: Literature Reviewmentioning

confidence: 99%

A set packing inspired method for real-time junction train routing

Lusby

Larsen

Ehrgott

et al. 2013

Computers & Operations Research

View full text Add to dashboard Cite

show abstract

“…Thus, it is di cult to determine the optimum solutions to industry-sized problems in a reasonable time, and this raises the need for e cient heuristic and meta-heuristic algorithms. In the related literature, several sophisticated search procedures are introduced, such as look-ahead search [8], backtracking search [9], and meta-heuristics algorithms [10][11][12]. D'ariano et al [13] addressed the real-time train scheduling problem in a railway network using an e cient Branch and Bound (B&B) algorithm.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated train scheduling and infrastructure development model in railway networks

et al. 2017

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Abstract. The evaluation of the railway infrastructure capacity is an important task of railway companies. The goal is to nd the best infrastructure development plan for scheduling new train services. The question addressed by the present study is how the existing railway infrastructure can be upgraded to decrease the total delay of existing and new trains with minimum cost. To answer this question, a mixed-integer programming formulation is extended for the integrated train scheduling and infrastructure development problem. The train timetabling model deals with the optimum schedule of trains on a railway network and determines the best stop locations for both the technical and religious services. We developed two heuristics based on variable xing strategies to reduce the complexity of the problem. To evaluate the e ect of railway infrastructure development on the schedule of new trains, a sequential decomposition is adopted for Iranian railway network. The outcomes of the empirical analysis performed in this study allow to gain bene cial insights by identifying the bottleneck corridors. The result of the proposed methodology shows that it can signi cantly decrease the total delay of new trains with the most emphasis on the bottleneck sections.

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“…Consideration was given to the ways in which a journey might suffer a setback. This resulted in a set of journey breakdown scenarios as shown in Table 3 (see Adenso-Diaz et al 1999 for another list of possible incidents leading to rescheduling, or Higgins and Kozan 1998 for different delay types). An interpretation of the likely consequences, recovery options, and information needs for each scenario is also given in Table 3.…”

Section: Journey Breakdownsmentioning

confidence: 99%