Operational uncertainties play a critical role in determining potential pathways to reduce the building energy footprint in the Global South. This paper presents the application of a non-dominated sorting genetic (NSGA II) algorithm for multi-objective building design optimization under operational uncertainties. A residential building situated in a mid-latitude steppe and desert region (Köppen climate classification: BSh) in the Global South has been selected for our investigation. The annual building energy consumption and the total number of cooling setpoint unmet hours (h) were assessed over 13,122 different energy efficiency measures. Six Pareto optimal solutions were identified by the NSGA II algorithm. Robustness of Pareto solutions was evaluated by comparing their performance sensitivity over 162 uncertain operational scenarios. The final selection for the most optimal energy efficiency measure was achieved by formulating a robust multi-criteria decision function by incorporating performance, user preference, and reliability criteria. Results from this robust approach were compared with those obtained using a deterministic approach. The most optimal energy efficiency measure resulted in 9.24% lower annual energy consumption and a 45% lower number of cooling setpoint unmet h as compared to the base case.

As the construction industry undertakes complex and innovative projects, improving the labor productivity that helps accomplish the triple bottom line dimensions (time, schedule and performance) assumes greater importance. It is of even greater importance in developing countries like India where most of building construction takes place on a manual basis. In response, the purpose of this paper is to develop an evaluation framework for assessing the labor productivity in the construction industry. The application of the framework is demonstrated for the case of the Indian construction industry and key insights are reported. The literature review and expert survey has helped to identify 4 main dimensions and 14 different factors affecting labor productivity in the construction industry. An application of Decision-Making Trial and Evaluation Laboratory was deployed to investigate into the causality of labor productivity dimensions. The research delivers a quantitative labor productivity assessment framework for evaluating causal relationships among the factors influencing labor productivity in the construction industry. The result obtained for a typical case of the Indian construction industry showed that the safety at construction site has a profound effect on labor-related factors. The paper makes two contributions in the domain of labor productivity assessment in the construction industry. First, it proposes an evaluation framework to investigate into the causal relationships among the labor productivity factors. This proposed framework is robust and can be used to compare the performance of different construction projects using an integrated approach proposed by this research. Such exploratory analysis would assist benchmarking studies. Second, it undertakes an empirical investigation for the Indian construction industry to develop key managerial insights and extend policy-related recommendations.