Cities increasingly face challenges regarding climate change, urban densification, biodiversity loss and community needs. These challenges demand for adaptation strategies. Each neighborhood faces unique challenges, requiring locally adapted strategies. Public buildings, often owned by municipalities within a portfolio, can play a key role in these strategies to improve resilience and livability. Yet, due to the diversity and number of assets, deciding where and how to intervene is highly complex. Data and decision support tools such as Multi- Objective Optimization (MOO) and Multi-Criteria Decision Analysis (MCDA) can reduce this complexity, but traditional approaches often exclude stakeholders early, restrict evaluations to predefined solutions, commit aggregation errors, and typically end with Pareto fronts that lack a clear final design.
This thesis develops and tests a MOO decision support tool for the management of building portfolio assets. Based on the Preferendus framework, it integrates stakeholders a priori, avoids fixed solution spaces, applies mathematically sound preference modeling, and converges on a single optimal configuration. A demonstrator case with 25 buildings with data derived from The Hague was used to optimize rooftop interventions (sedum, biodiversity, solar, water retention, and social-commercial roofs), balancing conflicting objectives such as neighborhood needs, CO₂ impact, investment costs, and financial returns.
The tool was validated in a live workshop at Bress, where objectives, weights and preference curves were adjusted in real time to search for a final optimal design configuration. Results showed its ability to support portfolio owners in exploring trade-offs and aligning interventions with objectives. A review by Arcadis confirmed its potential, while also pointing to improvements for further development such as spatial impact modeling, data quality, a more user-friendly interface, and prioritization of assets over time.
In conclusion, this preference-based MOO tool demonstrates that rooftop allocation is an effective demonstrator case for sustainable portfolio asset management. Preferendus proves suitable for structuring complex decision-making and translating stakeholder preferences into optimized, data-driven design strategies, while overcoming key shortcomings in similar studies.

An MDP project performed in the scope of the Delta Futures Lab in collaboration with the Dragon Institute. The report delves into the enviromental threats the Mekong Delta is facing and provides a design framework to deal with these problems. Furthermore, a case study is chosen to display how the framework can be adopted in a practical setting, envisioning what a future looks like where the Mekong Delta co-exists with water.