Analysing equipment allocation through queuing theory and Monte-Carlo simulations in surface mining operations

Sembakutti, Dilip; Kumral, Mustafa; Sasmito, Agus P.

doi:10.1504/ijmme.2017.10003266

Cited by 6 publications

(4 citation statements)

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“…MC generates random realizations to find an appropriate solution to a stochastic problem (Shonkwiler and Mendivil 2009). Sembakutti et al (2017) proposed an approach to Ozdemir and Kumral (2018a) generated random variables from a probability distribution with MC for uncertain variables of a material handling system (e.g., loading time, hauling time, and payload).…”

Section: Monte Carlo Simulation (Mc)mentioning

confidence: 99%

Optimization of drill bit replacement time in open-cast coal mines

Uğurlu

Kumral

2019

Int J Coal Sci Technol

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To gain a competitive edge within the international and competitive setting of coal markets, coal producers must find new ways of reducing costs. Increasing bench drilling efficiency and performance in open-cast coal mines has the potential to generate savings. Specifically, monitoring, analyzing, and optimizing the drilling operation can reduce drilling costs. For example, determining the optimal drill bit replacement time will help to achieve the desirable penetration rate. This paper presents a life data analysis of drill bits to fit a statistical distribution using failure records. These results are then used to formulate a cost minimization problem to estimate the drill bit replacement time using the evolutionary algorithm. The effect of cost on the uncertainty associated with replacement time is assessed through Monte-Carlo simulation. The relationship between the total expected replacement cost and replacement time is also presented. A case study shows that the proposed approach can be used to assist with designing a drill bit replacement schedule and minimize costs in open-cast coal mines. Keywords Cost minimization Á Drilling operation Á Optimum replacement time Á Evolutionary algorithm Á Sensitivity analysis Á Monte Carlo simulation List of symbols a Scale parameter (Weibull distribution) b Shape parameter (Weibull distribution) C f Cost of failure replacement C p Cost of predicted replacement C t Total cost of expected replacement C tu Total cost of expected replacement per unit time EA Evolutionary algorithm MTTF Mean time to failure MWD Measurement while drilling N Natural numbers ROP Rate of penetration R tu Probability of a predicted replacement S p Mean of the unshaded area t e Expected length of a bit usage t f Failure time t p Predicted length of a bit usage

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Section: Monte Carlo Simulation (Mc)mentioning

confidence: 99%

Optimization of drill bit replacement time in open-cast coal mines

Uğurlu

Kumral

2019

Int J Coal Sci Technol

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“…En este marco, Zio (2012) sostiene que el método de simulación Monte Carlo es adecuado para modelar las incertidumbres en sistemas complejos, gracias a su capacidad para simular escenarios que se acercan a la realidad mediante el uso de números aleatorios. La simulación de Monte Carlo ha demostrado ser eficaz en la gestión de incertidumbres, como se evidencia en el estudio de caso realizado por Sembakutti et al (2017), donde se simularon los índices de producción y costos introduciendo un nivel de incertidumbre mediante esta técnica. Por su parte, Ugurlu y Kumral (2019) determinaron una política óptima de reemplazo para brocas de perforación utilizando modelos de reemplazo óptimos combinados con simulación de Monte Carlo, para lograr resultados más realistas.…”

Section: Introductionunclassified

Política Óptima de Mantenimiento para Intercambiadores de Calor de Placas Soldadas Utilizando la Simulación de Monte Carlo

Carrillo Palma

2024

Rev. Téc. Ing. Univ. Zulia.

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Los intercambiadores de calor de placas soldadas son equipos fundamentales para los procesos de esterilización en la industria farmacéutica, por lo que es crucial diseñar planes de mantenimiento eficaces para evitar fallos que puedan comprometer la confiabilidad de estos procesos. El objetivo de esta investigación fue determinar, mediante la simulación de Montecarlo, una política óptima de mantenimiento para estos intercambiadores. Se utilizó una metodología descriptiva, aplicada y transversal basada en un diseño de campo. Se estudiaron siete intercambiadores de calor en una planta farmacéutica, sirviendo como población y muestra. Los instrumentos de recolección de datos incluyeron la revisión de registros existentes y la validación por expertos. El estudio demostró que la distribución Weibull es una herramienta útil para modelar los tiempos de falla de los intercambiadores y reveló que el tiempo óptimo de reemplazo es de aproximadamente 1,7 años, con un costo mínimo asociado de US$2.139. Estos hallazgos resultan esenciales para la planificación eficaz del mantenimiento y reemplazo de los equipos, así como para la optimización de los recursos económicos. Sin embargo, se reconoce la necesidad de una muestra más grande y de más datos para reforzar estas conclusiones.

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“…Production equipment is often used in fleet management, namely transport and loading equipment [2]. Coal mining activities are used by various mechanical equipment, including excavators as loading tools, dump trucks as transportation means, and bulldozers as peeling equipment [3]. The coal mining project in the Kananai Block area in South Barito Regency, especially surface mining, is a capital-intensive business.…”

Section: Introductionmentioning

confidence: 99%

Fleet Management Simulation Using Queuing Theory to Achieve Coal Production Targets

2020

IJETER

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In the match factor, mining activity between the haulers and loader equipment dramatically affects each fleet's production. The purpose of this study is to simulate the sufficient number of transportation uses as an effort to achieve production targets using queuing theory. The research methods are quantitative and descriptive by analyzing the compatibility value of fleet, fleet production capability, queue number, and queue time. The data required is the time of the distribution, the distance from the mining front to the ROM, and the company's speed limit. The results of this research are beneficial to users of the transporter simulation to be applied based on the theory of the queue is six transport units in the Anggrek pit with a compatibility value of 1.01, five transport units in the Dahlia pit with compatibility value 0.98, and five units of transport in the pit Anggrek with compatibility value 1.04. The haulers' recommendations were made by allocating two haulers units from the pit Anggrek to the Pit Dahlia and Kenanga. Each simulation's production capability reached the monthly production target, namely fleet Anggrek of 50,416.45 tons, fleet Dahlia of 32,424.3 tons, and fleet Kenanga of 46,027.8 tons. Based on the study results, the achievement of production targets can be fulfilled by simulating the number of haulers usage and controlling fleet management's compatibility level on each working front.

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Analysing equipment allocation through queuing theory and Monte-Carlo simulations in surface mining operations

Cited by 6 publications

References 0 publications

Optimization of drill bit replacement time in open-cast coal mines

Optimization of drill bit replacement time in open-cast coal mines

Política Óptima de Mantenimiento para Intercambiadores de Calor de Placas Soldadas Utilizando la Simulación de Monte Carlo

Fleet Management Simulation Using Queuing Theory to Achieve Coal Production Targets

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