An Improved Time-cost Trade-Off Model with Optimal Labor Productivity

Optimization of the time-cost trade off (TCT) has received considerable attention for several decades. However, few studies have considered improving performance/productivity of existing crews. To shorten the gap to real-world applications, this study presents an improved TCT model that considers variable productivity using genetic algorithms (GAs). Through an illustrative case and a real world case, the results demonstrate that improving labor productivity of selected activities by allocating existing crews and management can yield an optimized solution. As such, a decision maker can implement a better optimized technique to reduce a project duration under budget while reducing the risk of liquidated damages. The main contribution of this study is to apply managerial improvement of labor productivity to TCT optimization, the project duration can be reduced owing to improved productivity of existing crews rather than inefficient overmanning, overlapping or costly substitution. In the end, three important managerial insights are presented and future research is recommended.