A Mathematical Model for Quantifying Workers’ Learning Range on Repetitive Construction Projects

Abstract

The role of planning and management in the construction industry has always been significant towards improving project schedules. However, the impact of planning strategies on different construction schedules poses a margin for development and enhancement, especially when it comes to managing linear schedules or schedules of projects undergoing repetitive types of work. Previous research efforts developed models to individually analyze the influence of congestion and the learning curve factors on linear schedules, but failed to capture the combined complexities and dynamics when integrating both. Therefore, this paper puts forward the groundwork of a scheduling optimization framework and presents work targeted at quantifying the learning development range of construction workers on repetitive projects. The ultimate goal is to minimize potential congestions by taking into account the inherent uncertainties of linear activities while considering the learning curve effect. More specifically, three dimensions of uncertainty are considered for each activity, namely at the level of the activity itself, at the level of the activity and its predecessors, and at the activity-network level. At the heart of the proposed mathematical model is a fuzzy-based system that generates a minimum percentage reduction in productivity boundaries for each activity with different uncertainty dimensions. The presented fuzzy system will, in future work, become the foundation of a time-cost optimization framework for linear scheduling methods.