## 2 Literature Review
### Stochastic Multiskilled Resources Scheduling (SMSRS)  
Despite the contributions of the previous studies to MSRS, projects continue to deviate from the planned baselines. Sometimes, these deviations may result from several risk factors, which include but are not limited to underestimation of work content, error in the activity duration estimated, bad weather, delay supply of material, lack of substantial information at the early stage of the project, etc. [33]. Other situational risks that may constitute uncertainty in activity duration is the introduction of new technology, external regulations, and pressure from the client to complete the project earlier than the planned completion date. These risks and several assumptions (implicit or explicit) made during the development of the schedule called for robust resource scheduling. A robust resource schedule accounts for various risks and uncertainties that might influence the project delivery.  
Numerous studies have proposed different SMSRS models. [PERSON] and [PERSON] [34] examined multi-objective functions for project scheduling with stochastic activity durations using a greedy randomized adaptive search procedure (GRASP) to minimize project makespan. It was established in the study that the stochastic models are better than the deterministic model. [PERSON] et al. [35] also investigated the impact of resource breakdown on activity duration focusing only on resource failure and repair times using a stochastic model. The study revealed that stochastic scheduling offers more advantage of providing a good time buffer to accommodate for variability in activity durations. [PERSON] [36] proposed a bi-objective multi-mode RCPSP model considering risk and uncertainty based on an analytical (mathematical) method. The model was used to generate random activity durations and cost estimates. Similarly, [PERSON] and [PERSON] [37] applied a stochastic resource-constrained project schedule problem (SRCPSP) model to obtain a near-optimal solution for RCPSP based on a computationally tractable closed-loop approximate dynamic programming. Additionally, [PERSON] et al. [38] developed two stochastic optimization approaches for multi-skilled scheduling namely the metaheuristic and mixed-integer programming methods to address the uncertainty on work package processing times. External cost reduction was achieved in this study using external human resources to augment the shortage of internal human resources.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Priority Rule** & **Activity Listing Order** \\ \hline Shortest Activity Duration (SAD) & Activities are listed in increasing order of SAD \\ \hline Longest Activity Duration (LAD) & Activities are listed in decreasing order of LAD \\ \hline Earliest Start Time (EST) & Activities are listed in increasing order of EST \\ \hline Earliest Finish Time (EFT) & Activities are listed in increasing order of EFT \\ \hline Latest Start Time (LST) [11] & Activities are listed in increasing order of LST \\ \hline Latest Finish Time (LFT) & Activities are listed in increasing order of LFT \\ \hline Minimum Slack (MSLK) & Activities are listed in increasing order of MSLK \\ \hline \hline \end{tabular}
\end{table}
Table 1: Priority rules frequently used.