Project Overview

Climate change is rapidly increasing the frequency and severity of extreme weather events. The Netherlands, where 26% of the territory lies below sea level, is particularly vulnerable to flooding and other climate-related hazards. Effective a priori evacuation plans are known to reduce loss of life, yet uncertainty surrounding both the spatial development and temporal progression of disasters often renders theoretically optimal evacuation decisions unviable in practice. Designing evacuation strategies that remain efficient, feasible and resilient when critical infrastructure is threatened by large-scale disruptions therefore remains a major challenge in Operations Research.

The RESCUE project develops new multi-stage optimization models for pre-disaster evacuation decisions on critical road and shelter infrastructure subject to failures. Combining robust optimization, stochastic programming and copula theory, the project assesses the resilience of both self-evacuation and shelter-in-place strategies. For this purpose, the project draws on concepts from network flow problems and assignment problems. Models are validated using hydrodynamic simulations of Dutch flash flood scenarios.

Project Context

RESCUE (The Resilience of Evacuation Strategies on Compromised and Unreliable Evacuation infrastructure) is a project from the Vrije Universiteit Amsterdam (VU), funded by the NWO Open Competition - Domain Science. The PhD candidate will be employed at the Department of Operations Analytics and supervised by Dr. Pascal Wissink (PI), in close collaboration with Dr. Markus Leitner, Prof. David Wozabal and Prof. Bas Kolen (University of Amsterdam).

Your Duties

• Conduct high-quality doctoral research leading to journal articles and a PhD dissertation
• Develop optimization models, algorithms, and computational methods for robust evacuation planning
• Present your work at scientific conferences and workshops with academic and industry partners
• Participate in PhD training courses, with the opportunity to obtain a PhD teaching qualification certificate
• Contribute to the teaching activities of the department (lectures, tutorials, exams, grading, etc.)
• Work closely with the supervisory team and help translate results into practical recommendations for disaster preparedness

Your Profile

• A master’s degree in Operations Research, Computer Science, Applied Mathematics, Econometrics, Industrial Engineering, or a closely related field with a strong quantitative component
• Strong interest in optimization, mathematical programming and computational methods
• Prior knowledge of stochastic programming or (distributionally) robust optimization is an advantage
• Affinity with network resilience, climate resilience, or flood risk applications is also a plus
• Strong programming skills in any (object-oriented) programming language
• Prior experience with optimization solvers (Gurobi or CPLEX) and simulation tools is an advantage
• Strong analytical writing skills and the ability to present technical results clearly
• Excellent command of English in both speaking and writing