Abstract

This paper studies the robust cyclic timetabling problem. The goal is to modify a given reference timetable to enhance its robustness against small stochastic disturbances. The robustness is measured by the expected total delay of the realised timetable. Kroon et al. (Transp Res Part B 42(6):553–570, 2008) propose a stochastic programming approach and implement it for Netherlands Railways (NS). While the model’s outcome is accepted by practitioners, relevant planning problems are rendered intractable by computation times of up to several days. In this paper we describe a Branch-and-Bound algorithm for solving the stochastic program of Kroon et al. (Transp Res Part B 42(6):553–570, 2008). We propose specific node selection rules, variable selection rules, constructive heuristics and lower bounds. We carry out computational tests on large real-life problem instances. The results confirm that our algorithm is able to considerably improve the robustness of the reference solutions. This is achieved with computation times of a few minutes. However, the weak lower bounds we use leave a considerable optimality gap. Therefore, our algorithm is best described as a heuristic solution method.

Highlights

  • In this paper we study the robust cyclic timetabling problem

  • In this paper we propose a Branch-and-Bound algorithm for solving the robust cyclic timetabling problem

  • We discuss the remaining major steps of the approach in details, and we propose a particular rule for node selection, variable selection as well as for a constructive heuristic

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Summary

Introduction

In this paper we study the robust cyclic timetabling problem. We aim at modifying a given reference timetable in such a way that the sum of the realised delays is minimised when operating it subject to stochastic travel times. Our main focus lies in designing a pragmatic approach to solving large, practically relevant cases

Maroti
Reference timetable
Modified timetable
Linear timetable
Delays and robustness
Simulation algorithm
Computing the nodes’ lower bounds
Node selection
Variable selection
Finding feasible solutions
Constraint propagation
Overview of the best solution
Computation times
Node selection and variable selection rules
Findings
Conclusions and future research

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