Delay Risk Assessment of Repetitive Construction Projects Using Line-of-Balance Scheduling and Monte Carlo Simulation

Although the line-of-balance (LOB) method is widely used for the scheduling of repetitive construction projects, there are only a limited number of studies that deal with the issue of how to incorporate uncertainty in repetitive schedules. In this paper, a delay risk assessment method is proposed for projects scheduled by LOB. In the proposed method, a LOB schedule is prepared considering the target rate of delivery, and then risk scenarios are defined considering the sources of uncertainty and vulnerability of activities. Next, probability distributions are determined for the required number of labor-hours for each activity and for learning rates, and finally, the delay risk of the project is quantified using Monte Carlo simulation. The application of the method, its outputs, and its potential benefits are demonstrated by a hypothetical case study. Moreover, the proposed method is applied to a real high-rise building project and the potential benefits of using a probabilistic LOB schedule at the planning stage are investigated. The findings reveal that the outputs of the proposed method may enable decision makers to estimate delay risk under various scenarios, formulate effective risk response strategies, and prepare contingency plans for resource utilization in repetitive tasks. However, the simplifying assumptions made during the development of the proposed model regarding the relationships between activity durations, risk scenarios, and learning rates may limit its applicability for all project conditions. Yet the method is generic, and its assumptions can be revised in consideration of specific project characteristics and objectives.


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Citation Formats
O. B. Tokdemir and İ. Dikmen Toker, “Delay Risk Assessment of Repetitive Construction Projects Using Line-of-Balance Scheduling and Monte Carlo Simulation,” JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, pp. 0–0, 2019, Accessed: 00, 2020. [Online]. Available: