1077. Game-theoretic Tree Regression for Surrogate-based Optimisation of Active Pharmaceutical Ingredient Manufacturing
Invited abstract in session MB-4: Interpretable Optimization Methods and Applications, stream Data Science meets Optimization.
Monday, 10:30-12:00Room: Rupert Beckett LT
Authors (first author is the speaker)
| 1. | Artemis Tsochatzidi
|
| Chemical Engineering, University College London | |
| 2. | Francesca Cenci
|
| GlaxoSmithKline (GSK) | |
| 3. | Magdalini Aroniada
|
| GlaxoSmithKline (GSK) | |
| 4. | Lazaros Papageorgiou
|
| Chemical Engineering, University College London |
Abstract
Modern industries rely on advanced modelling techniques to enhance process efficiency, yet the computational complexity of these models often limits their direct use in optimisation. Surrogate-based approaches provide an effective alternative, enabling efficient exploration of the solution space. In this work, we introduce a game-theoretic tree regression approach for surrogate-based optimisation, integrating a multi-target tree regression model to approximate complex process dynamics. The proposed approach formulates optimisation as a strategic decision-making problem, to reveal trade-offs between conflicting objectives such as yield and purity in Active Pharmaceutical Ingredient (API) manufacturing. By leveraging Pareto fronts and Nash equilibrium, the methodology provides a structured mechanism for navigating these competing goals and deriving optimal operational strategies. The proposed approach enhances interpretability, computational efficiency, and practical applicability, offering a powerful tool for decision-making in pharmaceutical manufacturing and beyond.
Keywords
- Game Theory
- Industrial Optimization
- Mathematical Programming
Status: accepted
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