Merits of Discrete Event Simulation in Modeling Mining Operations

"Discrete event simulation is a stochastic mathematical modeling tool with applications in queuing systems. Many mining operations, both surface and underground, can be simulated in the context of queuing theory , for example open pit loading and haulage operation, underground level and vertical transportation, and mineral processing circuits. A computer simulation model of the operation is an invaluable tool to study both the system’s dynamics and conducting sensitivity analysis on different effective elements on system performance. In this paper, merits of discrete event simulation in decision making in mining engineering is discussed. Moreover, a stochastic simulation framework for selection and sizing of shovels and dump trucks in surface mining is presented to elaborate one example for applicability of the method in mining operations. INTRODUCTION Mathematical modeling is a valuable tool in design, optimization, and decision making in both surface and underground mining operations. Several researchers have tried to study different aspects of mining operations using a wide range of mathematical techniques such as linear programming[1], analytical hierarchy process [2], non-linear programming[3], genetic algorithms[4-5] ,mixed integer programming [6], machine repair model[7], queuing theory [8], and conventional spreadsheet calculations based on experience and engineering judgments. There are different uncertainties in typical mining operations (e.g. material loading and haulage, drilling, and blasting). For example, in a truck-shovel operation, different governing activities such as loading, haulage, and dumping cycles are stochastic variables. Moreover, analytical modeling of a large truck-shovel operation is very complicated. Thus, application of analytical and/or deterministic techniques cannot guarantee a solution for different problems that are encountered in daily mining operations. Discrete-event system simulation (DES) is a modeling method for such time discrete and probabilistic phenomena [9]. DES has been employed by different researchers in mining engineering through available software and languages such as GPSS, SIMAN- ARENA, and SLAM [10-14]. Most of the studies so far, tried to evaluate some “what-if” scenarios, to understand the possible effects of changing different input variables on overall mine economics. For instance, Baffi and Ataeepur (1996) used Arena to simulate a truck-shovel operation [10]. Also, Sturgul has conducted extensive studies in the application of DES in the mining engineering field [15- 17]. A very good background review of application of this technique in the industry can be found in [15-16]. Previous studies have shown that, different approaches including deterministic, stochastic, and experimental methodologies result in considerable differences in outputs [18]. These techniques lead to different solutions, regardless of the quality of technique/software itself or knowledge of the modeling team. Hence, the first step is to develop a comprehensive simulation framework to obtain nearly the same optimal results for the same input variables, regardless of the employed technique."