Robust decision making for future train maintenance

Robust decision making for future train maintenance: Illustrating the abilities of decision making under deep uncertainty within the train maintenance context

Tunca, Kerem (TU Delft Technology, Policy and Management)

Nikolic, I. (mentor)
Zatarain Salazar, J. (mentor)
Milde, T. (mentor)

Delft University of Technology

Engineering and Policy Analysis

2023-04-13

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.

Train Maintenance
Robust Decision Making
Agent-Based Modeling
Scenario Discovery
Decision Making under Deep Uncertainty

http://resolver.tudelft.nl/uuid:063165eb-1d2a-46d6-9319-0c0c41aedd9e

Student theses

master thesis

© 2023 Kerem Tunca