How to Measure the Robustness of Shunting Plans

Authors Roel van den Broek, Han Hoogeveen, Marjan van den Akker

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Author Details

Roel van den Broek
  • Department of Computer Science, Utrecht University, Utrecht, The Netherlands
Han Hoogeveen
  • Department of Computer Science, Utrecht University, Utrecht, The Netherlands
Marjan van den Akker
  • Department of Computer Science, Utrecht University, Utrecht, The Netherlands

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Roel van den Broek, Han Hoogeveen, and Marjan van den Akker. How to Measure the Robustness of Shunting Plans. In 18th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems (ATMOS 2018). Open Access Series in Informatics (OASIcs), Volume 65, pp. 3:1-3:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


The general problem of scheduling activities subject to temporal and resource constraints as well as a deadline emerges naturally in numerous application domains such as project management, production planning, and public transport. The schedules often have to be implemented in an uncertain environment, where disturbances cause deviations in the duration, release date or deadline of activities. Since these disruptions are not known in the planning phase, we must have schedules that are robust, i.e., capable of absorbing the disturbances without large deteriorations of the solution quality. Due to the complexity of computing the robustness of a schedule directly, many surrogate robustness measures have been proposed in literature. In this paper, we propose new robustness measures, and compare these and several existing measures with the results of a simulation study to determine which measures can be applied in practice to obtain good approximations of the true robustness of a schedule with deadlines. The experiments are performed on schedules generated for real-world scheduling problems at the shunting yards of the Dutch Railways (NS).

Subject Classification

ACM Subject Classification
  • Computing methodologies → Planning under uncertainty
  • robustness
  • resource-constrained project scheduling
  • partial order schedule
  • uncertainty
  • Monte Carlo simulation
  • train shunting


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