Occupational accidents in the construction industry occur frequently when compared with other industries. Construction accidents still cannot be prevented at the desired level. These accidents have been explored for prediction purposes using traditional statistical and machine-learning techniques. However, some challenges regarding imbalanced and multicollinearity problems present in the dataset are not considered while analyzing large and categorical variable-rich data. For this reason, this study aims to predict the severity of non-fatal construction accidents considering these challenges to obtain accurate results. In this study, standard binary logistic regression with Firth, Ridge, Lasso, and Elastic Net regularized models were utilized for the prediction of lost workdays in the construction industry. The data used were classified into five groups: victim, workplace, accident time, accident and sequence of events, and post-accident state-related variables. The results showed that Firth’s logistic model is the best-performing model and age, education, vocational education, size of the workplace, project type, working environment, accident month and year, general and specific activities, material agent of deviation, type of injury, and part of body injured are the most significant variables. This study by providing interpretable machine learning tools is the first attempt to use proposed models in the construction safety field.

In this research, the application of two scalarization methods, namely the conic scalarization method and the $\varepsilon$-constraint method, is investigated within the context of a multi-objective optimization problem. These methods are used to address the challenge of assigning nurses to patients on a hospital unit during a shift. The two objective functions of this assignment problem are based on patient workload metrics and unit-related travel distance measures. The proposed solution approach demonstrates the ability to generate solutions that eluded the previous mathematical programming techniques that relied on simplistic weightings of conflicting objective functions. In addition, it is found that the $\varepsilon$-constraint method outperforms the conic scalarization method in terms of solution time. This work contributes to enhancing our understanding of effective strategies for addressing complex workload distribution challenges within healthcare settings.