The Indoor Environmental Quality: A TOPSIS-based approach with indirect elicitation of criteria weights

The Indoor Environmental Quality (IEQ) assessment is a hot topic both for designers of industrial buildings and for academics since it has been proven to affect workers’ productivity. Despite the advantages of indirect eliciting approaches, only direct eliciting is used in the literature to assign weights to the main risks included in the IEQ assessment, i.e., those referring to the thermal comfort, visual comfort, acoustic comfort and indoor air quality. In order to bridge this gap and in line with the drivers of the human-centric industrial revolution, we have developed an indirect eliciting approach based on logistic regression and integer optimization that indirectly derives the aforementioned weights per worker (i.e., individual weighting) on the basis of the overall comfort perceived by him/her in different reference scenarios. These weights are then used to compute a TOPSIS-based risk measure that maps the aggregated, individual and dynamic risks to which the worker is subjected over time. A real case study is used to validate our proposal. The achieved results highlight the superiority of our indirect eliciting approach compared to the Analytical Hierarchic Process in reconstructing the overall comfort perceived by workers, as well as that age plays a crucial role to assign weights to the main risks included in the IEQ.The Indoor Environmental Quality (IEQ) assessment is a hot topic both for designers of industrial buildings and for academics since it has been proven to affect workers’ productivity. Despite the advantages of indirect eliciting approaches, only direct eliciting is used in the literature to assign weights to the main risks included in the IEQ assessment, i.e., those referring to the thermal comfort, visual comfort, acoustic comfort and indoor air quality. In order to bridge this gap and in line with the drivers of the human-centric industrial revolution, we have developed an indirect eliciting approach based on logistic regression and integer optimization that indirectly derives the aforementioned weights per worker (i.e., individual weighting) on the basis of the overall comfort perceived by him/her in different reference scenarios. These weights are then used to compute a TOPSIS-based risk measure that maps the aggregated, individual and dynamic risks to which the worker is subjected over time. A real case study is used to validate our proposal. The achieved results highlight the superiority of our indirect eliciting approach compared to the Analytical Hierarchic Process in reconstructing the overall comfort perceived by workers, as well as that age plays a crucial role to assign weights to the main risks included in the IEQ