Robust decision making for future train maintenance

Illustrating the abilities of decision making under deep uncertainty within the train maintenance context

Master thesis (2023)

Authors

K. Tunca Technology, Policy and Management

Contributors

I. Nikolic System Engineering - TPM (supervisor 1)
J. Zatarain Salazar Policy Analysis - TPM (supervisor 1)
T. Milde Nederlandse Spoorwegen (supervisor 1)
Faculty
Technology, Policy and Management
Copyright: © 2023 Kerem Tunca
Agent-Based Modeling Scenario Discovery Robust Decision Making Decision Making under Deep Uncertainty Train Maintenance
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Published Date

13-04-2023

Language

English

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Faculty

Technology, Policy and Management

Abstract

This research has illustrated the abilities of incorporating robust decision making within train maintenance. Both scheduled and unscheduled maintenance processes of the Nederlandse Spoorwegen (NS) have been examined, after which exploratory modeling and analysis enabled developing valuable insights for decision makers. Scenario analysis through simulation allowed for the quantification of the effect of deep uncertainty in relation to train maintenance. From the scenario analysis it is discovered what uncertainties are, according to the simulation model, of significant influence towards maintenance performance. The results presented allow decision makers to support their decisions when evaluating which type of policies to perform within the next decade. While the outcomes are specific for NS, the aim is to show that the approach held in this research is also applicable throughout other fields where decision making information quality should be improved.