## 2 Literature Review
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Authors** & **Contribution** \\ \hline \hline \multicolumn{4}{p{113.8 pt}}{To improve the performance of critical path (pre-project) activity scheduling, [PERSON] et al. [67] proposed a method that integrates Bayesian networks to estimate the conditional delay probability of an activity based on its predecessor. If yields much better results than the traditional Monte Carlo simulations (by 52%). The [PERSON] et al. [67] method relies on data that originates from 302 completed infrastructure construction projects; [PERSON] et al.’s [67] model was appropriately trained/calibrated and validated on a large infrastructure road construction project. It works well in predicting project delays. \\ \hline \hline Monte Carlo simulation and Bayesian network & [PERSON] et al. [66] & [PERSON] et al. [66] brought together the Bayesian network and Monte Carlo simulation methods and presented the timing of a construction project in the context of a framework for assessing the overall impact of risks in such a project. \\ \hline Confidence based scheduling procedure (CBSP) & [PERSON] and [PERSON] [1] & [PERSON] and [PERSON] [1] propose a method to determine the probability distribution of project completion times by estimating the duration of individual tasks/activities using confidence-based estimation. \\ \hline \hline \multicolumn{4}{p{113.8 pt}}{Robustness} & [PERSON] and [PERSON] [62]; [PERSON] and [PERSON] [64]; [PERSON] et al. [65]; [PERSON] [63] & As one of the methods, [PERSON] et al. [65] presented robustness, and more precisely, proactive heuristic methods for robust project scheduling. According to [PERSON] and [PERSON] [64], robustness of a schedule means that its validity is maintained despite small changes in the duration of processes (activities) and these changes are due to risks. The development of robust methods is described by [PERSON] and [PERSON] [62]. Predictive scheduling with a proactive approach is about creating schedules that are robust to disturbances hence the name—robust schedules). \\ \hline \hline \multicolumn{4}{p{113.8 pt}}{Markov Dynamic PERT Model} & [PERSON] and [PERSON] [39] & Refinement of project time duration uncertainty bounds; this approach estimates the influence of factors such as war, strikes and inflation that make activity durations non-static over time. This is an untested model, but it combines externalities, deterministic CPM, PERT and correlation in an interesting way. \\ \hline \hline \multicolumn{4}{p{113.8 pt}}{CSRAM accounts for covariance and correlation effects with PERT and CPM; it uses simple subjective inputs for a number of project risks, including weather factors, soil conditions, labour productivity and material/resource availability. However, CSRAM does not address the key problem of subjectivity and opinion-based analysis, a factor commonly associated with contract disputes [69; 70]. Furthermore, there is a lack of empirical evidence that this technique is scalable and works across different types of projects. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._(external factors are unpredictable and cannot be controlled) [73]. [PERSON] and [PERSON] [74] emphasise the importance of a quality schedule and attribute to it an important role in reducing delays in project implementation and achieving agreed outcomes and benefits (deliverables) [75]. A high-quality schedule is interpreted by the above authors as an optimal and feasible plan of activities. The quality of a schedule can be improved by contrasting expected results with resource and spatial constraints and technical knowledge, avoiding subjectivity and uncertainty by optimising all activities (groups of activities) to produce an achievable schedule/action plan [74]. Good scheduling technique aims to minimise the possibility of project delays [10].