Optimization of Multi-Skilled Labor with the Application of Partial Allocation of Resources

The current practice of labor allocation in construction assumes single-skilled workforce; meaning that each worker is assumed to be skilled in one trade. In such practice, at any instance in the project lifecycle, some of the activities may be delayed due to the unavailability of a certain type of workforce that is engaged in other activities. Due to the inefficiencies of these solutions, some research discussed the idea of “multi-skilled” labor, where some of the workers may have enough training to carry out different activity types. Yet, the literature falls short in providing a robust multi-skilling framework; specifically, one that solves the problem of partial allocation of labor. Unlike all the previous multi-skilling research, the implementation of partial allocation enables secondary resources to be deployed at any day of the activity’s duration as opposed to only allocating resources that are idle during the full duration of said activity. The objective of this research is to develop a framework for optimizing the allocation of multi-skilled labor in construction projects by allowing partial allocation. The novelty of the presented methodology is that it allows for partial allocation, which further minimizes the idle times of labor when compared to a conventional multi-skilled labor allocation model. With the use of genetic algorithms, the model was applied to a case study, where the results did not only show a reduction in idle labor days, but also shifted the histogram’s end point to the left, reducing some activities’ durations and hence reducing the total project duration.