Addressing Problem Drift in UNHCR Fund Allocation

Authors Sameela Suharshani Wijesundara , Maria Garcia de la Banda , Guido Tack

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Author Details

Sameela Suharshani Wijesundara
  • Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Australia
  • ARC Industrial Training and Transformation Centre OPTIMA, Clayton, Australia
Maria Garcia de la Banda
  • Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Australia
  • ARC Industrial Training and Transformation Centre OPTIMA, Clayton, Australia
Guido Tack
  • Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Australia
  • ARC Industrial Training and Transformation Centre OPTIMA, Clayton, Australia

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Sameela Suharshani Wijesundara, Maria Garcia de la Banda, and Guido Tack. Addressing Problem Drift in UNHCR Fund Allocation. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 37:1-37:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


Optimisation models are concise mathematical representations of real-world problems, usually developed by modelling experts in consultation with domain experts. Typically, domain experts are only indirectly involved in the problem modelling process, providing information and feedback, and thus perceive the deployed model as a black box. Unfortunately, real-world problems "drift" over time, where changes in the input data parameters and/or requirements cause the developed model to fail. This requires modelling experts to revisit and update deployed models. This paper identifies the issue of problem drift in optimisation problems using as case study a model we developed for the United Nations High Commissioner for Refugees (UNHCR) to help them allocate funds to different crises. We describe the initial model and the challenges due to problem drift that occurred over the following years. We then use this case study to explore techniques for mitigating problem drift by including domain experts in the modelling process via techniques such as domain specific languages.

Subject Classification

ACM Subject Classification
  • Theory of computation → Constraint and logic programming
  • Software and its engineering → Constraint and logic languages
  • Fund Allocation
  • Problem Drift
  • Domain Specific Languages
  • MiniZinc


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