A genetic algorithm based framework to model the relationship between building renovation decisions and occupants’ satisfaction with indoor environmental quality

Abstract

Providing comfortable indoor environmental conditions that please the majority of building occupants is an essential goal of facility management. The relationship between occupants' level of satisfaction with indoor environment conditions and their productivity at the workplace is well proven; however, the relationship between building renovation decisions and occupants’ level of satisfaction with indoor environmental quality (IEQ) is under-studied. To address this gap, this paper explores the relationship between these two factors. The work entails developing a genetic algorithm (GA) based framework to model the relationship between potential building retrofit options and improvement in IEQ conditions and associated level of productive time of building occupants. The framework takes into consideration the fluctuations in IEQ perception among different occupant groups and the corresponding levels of satisfaction. Subgroups of employees are classified based on three main parameters: gender, type of office, and distance to the nearest window. Based on these parameters, the increase in the level of satisfaction of building occupants, and consequently their productivity, as a result of building renovation are calculated and fed into a GA based decision making tool. The proposed generic framework and tool are tested on a real-world case study yielding optimal building retrofit options in light of several user-defined constraints such as available budget and market conditions.