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

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%.