Brief description of the PhD project:

PhD on the topic "Sequential decision strategies for complex optimisation problems under uncertainty and disruptions" [working title]

Innovations—such as emerging technologies, increasing digitization, and the widespread adoption of generative AI—continuously reshape the planning landscape in production and transportation systems, thereby necessitating the ongoing development of novel approaches. To account for these challenges, this subproject studies optimisation methods for complex decision problems where the order of decisions serves as a strategy to maintain feasibility under uncertainty and disruption. The project draws on established areas of Operations Research such as robust optimisation, stochastic and multi-stage optimisation, dynamic programming, and re-optimisation. The common question is how these methods can be used, adapted, or combined to obtain both a high-quality initial solution and an effective procedure for updating that solution after disruptions. In these settings, the sequence in which decisions are made is a structural component of the solution method that significantly influences feasibility, solution quality, and computational performance. The research explores how these methods can be adapted or combined to obtain both a high-quality initial solution and an effective re-optimization procedure for updating that solution after disruptions.

The project is embedded in the NACHOS project, which investigates how innovations can succeed from a technical, economic, and social perspective and aims to link social, cultural, and economic factors in the introduction of innovations. In this setting, the project contributes the Operations Research perspective on decision support for systems that must remain effective when conditions change. This is also directly relevant to Industry 5.0, which the European Commission frames in terms of human-centricity, resilience, and sustainability rather than efficiency alone.