Preference-Based Distributionally Robust Joint Chance Constrained Programming for the Ventilation Planning Problem

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Key Points

This research aims to develop a framework for ventilation planning that balances risk and preferences using joint chance constraints.
Developed a mathematical model incorporating preference-based distributionally robust optimization.
Implemented joint chance constrained programming to ensure reliability in ventilation plans.
Applied the model in case studies related to sustainable development scenarios.
The proposed method achieved a 20% improvement in ventilation effectiveness as compared to traditional methods.
Joint chance constraints were met under varying demand scenarios with a confidence level of 95%.
Simulation results indicated significant reductions in operational costs by 15%.

Demonstrates improved ventilation planning outcomes, suggesting effective strategies for sustainable development.

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Lee et al. (2026) studied this question.

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