PhD Optimizing evacuation decisions under infrastructure failure

Your function

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.

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 include:

• 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.

• 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.

As a university, we strive for equal opportunities for all, recognising that diversity takes many forms. We believe that diversity in all its complexity is invaluable for the quality of our teaching, research and service. We are always looking for talent with diverse backgrounds and experiences. This also means that we are committed to creating an inclusive community so that we can use diversity as an asset.

We realise that each individual brings a unique set of skills, expertise and mindset. Therefore we are happy to invite anyone who recognises themselves in the profile to apply.

A challenging position in a socially engaged organisation. At VU Amsterdam, you contribute to education, research and service for a better world. And that is valuable. So in return for your efforts, we offer you:

• a salary of € 3.059,00 (PhD) and maximum € 3.881,00 (PhD) gross per month in the fourth year, for a full-time employment
• an employment contract of initially 1 year. If there is sufficient perspective, this will be extended to a total of 4 years.

We also offer attractive fringe benefits and arrangements. Some examples:

• A full‑time 38‑hour working week comes with a holiday leave entitlement of 232 hours per year. If you choose to work 40 hours, you have 96 extra holiday leave hours on an annual basis. For part‑timers, this is calculated pro rata.
• 8% holiday allowance and 8.3% end‑of‑year bonus
• solid pension scheme (ABP)
• contribution to commuting expenses
• optional model for designing a personalised benefits package