###### Abstract  
This study stochastically predicted are production through discrete event simulation using four different probability density functions for truck travel times. An underground limestone mine was selected as the study area. The truck travel time was measured by analyzing the big data acquired from information and communications technology (ICT) systems in October 2018, and probability density functions (uniform, triangular, normal, and observed probability distribution of real data) were determined using statistical values. A discrete event simulation model for a truck haulage system was designed, and truck travel times were randomly generated using a Monte Carlo simulation. The ore production that stochastically predicted fifty times for each probability density function was analyzed and represented as a value of lower 10% (P10), 50% (P50), and 90% (P90). Ore production was underestimated when a uniform and triangular distribution was used, as the actual ore production was similar to that of P90. Conversely, the predicted ore production of P50 was relatively consistent with the actual ore production when using the normal and observed probability distribution of real data. The root mean squared error (RMSE) for predicting ore production for ten days in October 2018 was the lowest (24.9 ton/day) when using the observed probability distribution. mining; underground mine; truck-loader haulage system; Monte Carlo simulation; stochastic simulation; ore production +
Footnote †: journal: Medical  
The system operating parameters and truck cycle times are not constant or definitive for each haulage operation cycle and frequently fluctuate according to the working conditions [19]. For instance, the daily working time and number of dispatched trucks can fluctuate according to the ore production schedule. The truck cycle time may vary depending on the equipment breakdown, plant interruption, and haul road conditions. For these reasons, an uncertainty exists in the deterministic simulation method, which predicts the truck haulage system, using constant input values. One method to simulate the system model considering the variability of input parameters is to apply the stochastic simulation technique. This method randomly extracts the input values from the probability distribution of a random variable and predicts the distribution of the output factors through iterative simulations [20]. Until recently, several researchers applied stochastic simulation techniques to analyze optimal equipment combinations and design truck dispatch scenarios in both open-pit and underground mines [1; 19; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34]. In these studies, truck cycle time data were statistically analyzed and a probability density function was selected using probability distribution models, such as Gaussian, triangular, Weibull, gamma, lognormal, exponential, Erlang, and uniform distribution models (Table 1). The optimal probability distribution model was determined by evaluating the goodness-of-fit using the chi-squared test [35], Akaike information criterion (AIC) [36], and Kolmogorov Smirnov tests [37]. The ore production, equipment utilization rate, and equipment downtime corresponding to the truck-shoel combination were analyzed probabilistically [25]. In addition, the ore production was predicted based on the confidence level, using iterative stochastic simulation [30], and the prediction sensitivity was analyzed by alternating the probability distribution models of the truck cycle time data [32]. However, little attention has been paid to analyze and use the probability distribution derived from a large amount of truck cycle time data for stochastic simulations. The truck cycle time data were collected using the stopwatch method while boarding the equipment. For this reason, the theoretical probability distribution model was utilized in the stochastic simulations instead of the real statistical distribution pattern of the truck cycle time data.