... Carlo simulation then samples open and close dates from fitted probability distributions to quantify schedule and budget risks relative to this baseline. Findings Monte Carlo simulation of stochastic winter-road and thawing time windows for the Northwest Territories bridge case study shows an 84.78...

... a feasible contingency amount. Design/methodology/approach A quantitative methodology, integrating the Delphi and Monte Carlo simulation, is used to gather and analyze perspectives from 13 experienced professionals in medium-scale contracting. The research shows the advantages and disadvantages...

...Jinting Huang; Ankang Ji; Zhonghua Xiao; Limao Zhang Purpose The paper aims to develop a useful tool that can reliably and accurately find the critical paths of high-rise buildings and provide optimal solutions considering the uncertainty based on Monte Carlo simulation (MCS) to enhance project...

... within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis...

... and a probabilistic risk-based cost estimate Monte Carlo simulation were utilized to evaluate the data. The data were collected from numerous websites including OSHA, Centers for Disease Control and the World Health Organization. Findings The results show that as the monthly construction employment increased...

... computations are performed in two modules: (1) simulation module that integrates Monte Carlo simulation and a resource-driven scheduling technique to calculate the earliest crew deployment dates for all activities that fully comply with crew work continuity while considering uncertainty; and (2) optimization...

...% of the data, the model was developed in the form of a hybrid-based algorithm implemented through a suitable user-friendly graphical user interface (GUI) using MATLAB programming language. Bayesian model averaging, Monte Carlo simulation and fuzzy logic were the statistical methods used for the algorithm...

... values (NPVs) for governments and private investors are estimated via Monte Carlo simulation to better reflect the uncertainties. To further decide on the optimal length of the concession period, the principle of fair risk allocation between governments and private investors is adopted. The concession...

... (Molenaar et al., 2013). Uncertainty Risk Monte Carlo simulation Joint confidence level Mixed mode planning Risk register In the risk management process, the term “uncertainty” is often misused to replace the term “risk”. Although assessing uncertainty is considered to be a part...

... of the model. The model of the framework is developed by integrating the analytical hierarchy process (AHP) and Monte Carlo simulation (MCS) underpinned within an @Risk program. Findings – A case study is used to demonstrate the proposed methodology; the results found that the model helps to analyse...

... set by the project sponsor and the degree of project risks. Design/methodology/approach The proposed methodology combines the cumulative prospect theory (CPT) to characterize the risk preference of the project sponsor and the Monte Carlo simulation to assess the project riskiness. The methodology...

... and compare different options: © MCB UP Limited 2003 Risk management Probability calculations Estimation Buildings Monte Carlo simulation Project management Since the mid‐1980s many authors have suggested that the management of construction projects, large or small, benefits from...