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Modelling and Optimizing HVAC Systems for Early-Stage Building Design

Authors Victor Calixto , Camilo Cruz Gambardella , Amin Karimi , Pierre Le Bodic , Allen Z. Zhong

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Subject Classification

ACM Subject Classification
  • Mathematics of computing → Combinatorial optimization
Keywords
  • Application
  • Modelling
  • Operation Research & Mathematical Optimization

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Abstract

Heating, Ventilation, and Air Conditioning (HVAC) systems typically aim to regulate a building’s indoor environment. Many key design decisions which carry strong consequences on HVAC systems are made during early-stage building design, when architectural and structural layouts are still evolving. Early coordination between disciplines has the potential to minimise re-design of systems as a consequence of changes in other systems. This paper presents an optimization-based framework to support early design coordination among architectural, structural and mechanical designs, with a focus on ductwork layout. The generated layouts are intended to serve as initial candidate designs that engineers can further refine during later stages of the building design process. We develop models for generating feasible duct layouts accounting for structural constraints and cost objectives. The models are implemented in a high-level modelling language MiniZinc and solved in phases using Constraint Programming (CP) and Mixed-Integer Programming (MIP) solvers. Experiments on case studies show that feasible coordinated layouts can be generated, enabling iterative exploration of multiple alternative configurations during early-stage design.

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Victor Calixto, Camilo Cruz Gambardella, Amin Karimi, Pierre Le Bodic, and Allen Z. Zhong. Modelling and Optimizing HVAC Systems for Early-Stage Building Design. In 32nd International Conference on Principles and Practice of Constraint Programming (CP 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 379, pp. 10:1-10:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.CP.2026.10

Author Details

Victor Calixto
  • Building 4.0 CRC, Caulfield East, Victoria, Australia
  • Future Building Initiative, Monash Art, Design and Architecture, Monash University, Caulfield, Australia
Camilo Cruz Gambardella
  • Building 4.0 CRC, Caulfield East, Victoria, Australia
  • Future Building Initiative, Monash Art, Design and Architecture, Monash University, Caulfield, Australia
  • Department of Architecture, Monash University, Caulfield, Australia
Amin Karimi
  • Building 4.0 CRC, Caulfield East, Victoria, Australia
  • Department of Data Science and Artificial Intelligence, Monash University, Clayton, Australia
  • ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA), Melbourne, Australia
Pierre Le Bodic
  • Building 4.0 CRC, Caulfield East, Victoria, Australia
  • Department of Data Science and Artificial Intelligence, Monash University, Clayton, Australia
Allen Z. Zhong
  • Building 4.0 CRC, Caulfield East, Victoria, Australia
  • Department of Data Science and Artificial Intelligence, Monash University, Clayton, Australia
  • ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA), Melbourne, Australia

Acknowledgements

This research is supported by Building 4.0 CRC. The support of the Commonwealth of Australia through the Cooperative Research Centre Program is acknowledged. The authors would also like to acknowledge the industry partners A.G. Coombs Group, Podium Pty Ltd and Lendlease Ltd.

References

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