Erratum to “Using discrete event simulation cellular automata models to determine multi-mode travel times and routes of terrestrial suppression resources to wildland fires” [Eur. J. Oper. Res. 241 (2015) 763–770]

2015 ◽  
Vol 245 (1) ◽  
pp. 339-340
Author(s):  
Thomas J. Duff ◽  
Derek M. Chong ◽  
Kevin G. Tolhurst
Keyword(s):  
Cellular Automata ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Wildland Fires ◽  
Travel Times ◽  
Event Simulation ◽  
Multi Mode

scholarly journals Stochastic Predictions of Ore Production in an Underground Limestone Mine Using Different Probability Density Functions: A Comparative Study Using Big Data from ICT System

2021 ◽  
Vol 11 (9) ◽  
pp. 4301
Author(s):  
Dahee Jung ◽  
Jieun Baek ◽  
Yosoon Choi
Keyword(s):  
Big Data ◽  
Probability Distribution ◽  
Probability Density ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Real Data ◽  
Probability Density Functions ◽  
Density Functions ◽  
Travel Times ◽  
Event Simulation

This study stochastically predicted ore 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.

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