Optimizing ventilation system retrofitting: balancing time, cost, and indoor air quality with NSGA-III

Abstract

Improving ventilation systems is essential for better indoor air quality, energy efficiency, and overall building performance. This study introduces a new optimization model to tackle the trade-offs between time, cost, and indoor air quality (IAQ) in ventilation system retrofitting projects. Using the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the model evaluates various retrofitting options, including upgrades for ventilation capacity, energy efficiency, air quality, noise reduction, and aesthetic improvements. Each option is assessed for its impact on project duration, cost, and indoor air quality. The goal is to find the best combinations of these options that minimize both project time and cost while improving indoor air quality and meeting resource constraints. The NSGA-III algorithm generates a set of optimal solutions, providing a range of choices for balancing these factors. A comparison with existing methods shows that this new approach offers better solutions for managing these trade-offs. By selecting the most effective solution from these options using a weighted sum method, the study demonstrates NSGA-III’s power in handling complex optimization problems. This model supports better decision-making in retrofitting projects, advancing both sustainability and indoor environment quality.