## 1 Introduction
### Literature Review  
The volatility of construction conditions results in the variability of construction process execution time and even in uncertainties of the scope of work. To allow for the non-deterministic character of projects and their environment, the schedulers frequently treat process durations as random variables. Beta [7], logarithmic-normal [8], Weibull [9], trapezoidal [10], or triangular [11] distributions are thus used. The triangular distribution is argued to be the most intuitive to interpret [12; 13; 14]. Nevertheless, randomizing the durations of processes opens up the possibility to use simulation techniques to analyze the possible development of projects. The literature presents numerous simulation models of construction projects [15; 16; 17]. The model of [PERSON] and [PERSON] [18] combined the discrete event simulation with a simplified way of defining the critical path in the network. [PERSON] [19] used activity based construction simulation modeling that uses only one graphic symbol to represent a process. [PERSON] et al. [20] developed a stochastic simulation system (AS4), based on the CPM (Critical Path Method). [PERSON] et al. [21] presented an original method of planning projects in random conditions DESPEL (discrete event simulation with probabilistic event list); they accounted for resource availability constraints. [PERSON] [22] developed the stochastic project scheduling simulation (SPSS) to estimate the probability of meeting the due dates and the criticality of processes. [PERSON] [23] constructed his repetitive-projects evaluation and review technique (RPERT) that combines PERT with the line of balance. [PERSON] and [PERSON] [24] used simulations to assess the process criticality according to the process' impact on the project timelines. The model by [PERSON] and [PERSON] [25] enables resource leveling under uncertain activity durations and combines the Monte Carlo simulation method with genetic algorithms. [PERSON] and [PERSON] [26] compared the performance of several priority rules applied to scheduling a pipeline project; the simulation was to help select a priority rule for resource allocation most suitable from the point of timely project completion in random conditions. Two distinct approaches to random scheduling are observable in the literature: the \"offline\" and the \"online\" [27; 28]. The former, also referred to as proactive or predictive [29], consists of constructing a schedule that anticipates all possible future disruptions, with their identification resting upon the incomplete and uncertain information available before the start of the project. The latter is considered a continuous activity, with decisions on the processes' timing or scope, made and verified as the works progress and for a short planning horizon. The online scheduling uses the concepts of stochastic and reactive scheduling to create the schedule and update it. The proactive approach consists of designing robust schedules to be immune to the future and uncertain disturbances. It uses robust optimization techniques that focus on keeping the schedule acceptable (i.e., meeting all constraints) for all realizations of the uncertain durations within an uncertainty set. These techniques can be applied even if the probability distribution of the parameters cannot be defined, provided that the ranges of their values are known [30]. However, this approach is considered conservative: it may produce solutions much worse than expected or even infeasible. One of the ways to increase the schedule robustness is to allocate time buffers to processes using techniques based on contingency or redundancy. Some researchers put forward constructing a schedule in a traditional way (no risks considered), then adding time buffers at the end of all processes, and treating the buffers as an integral part of the expected process duration [31]. In this case, the time buffer does not prevent the propagation of disturbances throughout the schedule.