An Optimization Model to Address the Skilled Labor Shortage in the Construction Industry

Abstract

An optimization problem is formulated in this study to optimize the total investment in education, apprenticeship, and financial incentive to address the skilled labor shortages in the construction industry. Three separate mathematical models, namely linear programming (LP) models, fuzzy mathematical models, and structural equation models are compared for their accuracy, robustness, and ease of application in solving the optimization problem. LP models are found to be the best fit for solving the optimization problem. Relevant data on construction budget and investment preferences in education, apprenticeship, and financial incentive are obtained through a survey questionnaire from construction firms engaged in commercial and residential construction. The survey response suggests that more apprentices lean toward electrical and plumbing over carpentry due to the higher pay scale in both trade fields, thus reducing the skilled labor shortage in both areas when compared to carpentry. The optimization results indicate that apprenticeship and financial incentives play a significant role in reducing short-term labor shortages. Education, on the other hand, plays a significant role in reducing the labor shortage in the long run. A sensitivity analysis shows different optimal investments for the optimal allocation of labor shortages to be overcome by investments in education, apprenticeship, and financial incentives. A more robust analysis using additional data can be performed in future works to further investigate the relative effectiveness of investments in education, apprenticeship, and financial incentives in reducing skilled labor shortage.