Development of resource-constrained time-cost trade-off optimization model for ventilation system retrofitting using NSGA-III

The effective retrofitting of ventilation systems is essential for enhancing indoor air quality, energy efficiency, noise reduction, maintenance ease, aesthetics, and reducing the carbon footprint of buildings. This study presents the development of a resource-constrained time–cost trade-off optimization model for ventilation system retrofitting using the non-dominated sorting genetic algorithm III (NSGA-III). The model integrates various retrofitting options, categorized into ventilation capacity enhancement, energy efficiency improvements, air quality enhancements, noise reduction measures, maintenance facilitation, aesthetics improvements, and carbon footprint reduction strategies, each characterized by its retrofitting duration and associated cost. The objective is to identify optimal combinations of retrofitting options that minimize project completion time and cost while adhering to resource constraints. The NSGA-III optimization process generates Pareto-efficient solutions, providing decision-makers with a spectrum of optimal trade-offs. Model validation and performance metrics-based comparative analysis between the developed and existing models demonstrate the superior effectiveness of the proposed model in solving trade-off problems. The study employs a weighted sum method to select one solution from the set of Pareto-optimal solutions, illustrating the effectiveness of NSGA-III in balancing project timelines and costs. This research offers a robust methodological framework that enhances decision-making in the construction industry, contributing to global sustainable development goals.