Abstract
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.
Highlights
It is an established fact that projects are almost always behind schedule (Gerk & Qassim, 2008)
The results show that when improvement factor (Ix) performs the maximum output valued at 0.833 (1/1.2), the project duration will be reduced from 381.25 days to 361.38 days; the project direct labor cost increases from $17,513,000 to $17,591,183
The time-cost trade off (TCT) was typically executed by optimization techniques of crashing by increasing external resources, overlapping activities or substitution of external resources
Summary
It is an established fact that projects are almost always behind schedule (Gerk & Qassim, 2008). Determining how to complete projects on time and under budget has become the most important goal in the optimization field. Various studies have considered time-cost trade off (TCT) optimization using a variety of methods. Most researchers consider the straight relationship between time and cost no matter minimizing cost within granted duration or compressing schedule under budget. It is intuitively assumed that direct costs will increase with schedule acceleration. Such an assumption is deficient because labor productivity as a critical factor, which generally influences project time and cost is rarely considered when evaluating optimization of project execution. Even though current TCT optimizations could provide instant effect on improving project performance, side effects such as rework, fatigue or costly pay might be inevitable. The critical factor should be further considered in TCT optimizations
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