Optimisation of a real-time multi-period truck dispatching system in mining operations

Gurgur, Cigdem Z.; Dagdelen, Kadri; Artittong, Songwut

doi:10.1504/ijads.2011.038091

Cited by 24 publications

(14 citation statements)

References 3 publications

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“…De la misma manera es posible maximizar el VPN como función de la variación de la ley de corte sino también en los planes a corto y largo plazo de los camiones y las palas disponibles en las minas, que se generan en la operación minera con el fin de disminuir costos operativos para así aumentar la producción, teniendo en cuenta los parámetros económicos, periodos de tiempo, incertidumbre en los tiempos de carga y viaje, y las leyes del mineral [9].…”

Section: Costo De Remanejounclassified

NPV analysis as a function of the discount rate and cost of rehandling implementing SIMSCHED DBS to open pit mining

2017

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The function of all software is to model situations that look like reality, in order to find the most viable conditions for developing a mining project, since in these what is sought is to increase revenue and reduce costs by making better decisions. In this industry investors seek to obtain the highest income in exploiting underground resources, with the aim of achieving return on investment. In this article the discount rate and the cost of rehandling of a mineral deposit hypothetical gold and copper is evaluated using the SIMSCHED DBS software. In turn a search for information, which can give clarity to the concepts with which you are working, is done. Based on the simulations performed with the two economic variables you can select the optimal net present value (NPV) for future flows.Keywords: Rehandling cost; discount rate; NPV; Simsched DBS. ______________________________________________________________________________________________________________ Análisis del VPN en función de la tasa de descuento y el costo de remanejo implementando SIMSCHED DBS para una explotación minera a cielo abierto Resumen La función de todo software es modelar situaciones que se asemejan a la realidad, con el fin de encontrar las condiciones más viables para desarrollar un proyecto minero, lo cual implica incrementar ingresos y reducir costos mediante la toma de decisiones. En esta industria los inversionistas buscan obtener los ingresos más altos a la hora de explotar los recursos del subsuelo, con el objetivo de lograr el retorno a la inversión. En el presente artículo se evalúa la tasa de descuento y el costo de remanejo de un depósito mineral hipotético de oro y cobre, utilizando el software SIMSCHED DBS. A su vez se hace una revisión bibliográfica la cual permite dar claridad a los conceptos utilizados. Con base a las simulaciones realizadas con las dos variables económicas se pretende seleccionar el valor presente neto (VPN) más óptimo para los flujos futuros.Palabras clave: Costo de remanejo; tasa de descuento; VPN; Simsched DBS.

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Section: Costo De Remanejounclassified

NPV analysis as a function of the discount rate and cost of rehandling implementing SIMSCHED DBS to open pit mining

2017

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“…A better approach would be to optimise the operation by considering the actual capacities of every truck in the system and their respective payload. Gurgur, Dagdelen and Artittong (2011) propose an LP model for the shovel and truck allocations with an objective to minimise the deviation of the mine progress from the target provided by the MIP model. Although the model provides shovel assignments, it does so solely on strategic considerations (MIP model).…”

Section: Literature Reviewmentioning

confidence: 99%

“…With the exception of Gurgur et al (2011), to the best of the author's knowledge, no literature in the multi-stage dispatching discussed try to link the operational plans with the strategic plans of the mine. All those models try to improve the efficiency of the mining operations but miss to incorporate an important objective of production operations i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

Truck-shovel allocation optimisation: a goal programming approach

Upadhyay

Askari–Nasab

2016

Mining Technology

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Decision-making in mining is a challenging task. Optimal decisions regarding shovel and truck allocations, in consideration to the short-term production schedule, are very important to keep the operations inline with the planned objectives of the company in long term. This paper presents a mixed integer linear goal programming (MILGP) model to optimise the operations based on four desired goals of the company: (a) maximise production, (b) minimise deviations in head grade, (c) minimise deviations in tonnage feed to the processing plants from the desired feed, and (d) minimise operating cost. The model provides shovel assignments and the target productions; as an input to the dispatching system while meeting the desired goals and constraints of the mining operation. The model implementation with an iron ore mine case study provided average plant utilisation above 99%, average truck utilisation above 92% and average shovel utilisation above 95%.

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“…This is followed by a dispatch phase that assigns trucks to a specific shovel by considering ore production and equipment utilization [12]. Several simulation algorithms based on mixed-integer linear programming [13][14][15], queuing theory [2,[16][17][18][19], linear programming [20][21][22], goal programming [23], and stochastic programming [24] have been proposed for fleet allocation. Simulation techniques based on the assignment-problem [25,26] and transportation-problem [27] approaches have also been developed for truck dispatching.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

Baek

Choi

2020

Applied Sciences

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This paper proposes a deep neural network (DNN)-based method for predicting ore production by truck-haulage systems in open-pit mines. The proposed method utilizes two DNN models that are designed to predict ore production during the morning and afternoon haulage sessions, respectively. The configuration of the input nodes of the DNN models is based on truck-haulage conditions and corresponding operation times. To verify the efficacy of the proposed method, training data for the DNN models were generated by processing packet data collected over the two-month period December 2018 to January 2019. Subsequently, following training under different hidden-layer conditions, it was observed that the prediction accuracy of morning ore production was highest when the number of hidden layers and number of corresponding nodes were four and 50, respectively. The corresponding values of the determination coefficient and mean absolute percentage error (MAPE) were 0.99% and 4.78%, respectively. Further, the prediction accuracy of afternoon ore production was highest when the number of hidden layers was four and the corresponding number of nodes was 50. This yielded determination coefficient and MAPE values of 0.99% and 5.26%, respectively. truck-dispatch techniques have been proposed to simulate truck systems using real-time data collected from mining sites. For example, Tan et al. [39] identified discrete haulage-operation events by analyzing historical GPS truck-tracking data and implemented a truck-dispatch simulation algorithm using the Arena ® software. Chaowasakoo et al. [40] developed a real-time truck-dispatch system that plans truck-haulage strategies using truck-shovel activity-time data obtained from GPS.However, providing a comprehensive and effective truck-haulage systems simulation algorithm is difficult. First, open-pit mines are characterized by frequent changes in loading points during operations along with the use of multiple equipment-movement paths, unlike underground mines [1]. Additionally, several environmental factors, such as the weather, rock characteristics, and road conditions, affect haulage operations [12,41]. Thus, it is difficult to design a simulation algorithm for truck-haulage systems in large open-pit mines, where ore loading and haulage operations are simultaneously performed at multiple locations. Moreover, several types of simulation algorithms can be developed for a haulage plan that changes in real time, and it is difficult to alter these algorithms depending on variations in the haulage plan. Therefore, it is necessary to develop a simulation strategy that understands not only the characteristics of truck-haulage systems but also the sequence of discrete haulage operations. Additionally, the simulation technique must accurately predict ore production and equipment utilization using equipment-tracking data previously accumulated without use of prior-knowledge-based algorithms.Deep learning methods have recently attracted increased research interest because they facilitate easy analysis ...

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Optimisation of a real-time multi-period truck dispatching system in mining operations

Cited by 24 publications

References 3 publications

NPV analysis as a function of the discount rate and cost of rehandling implementing SIMSCHED DBS to open pit mining

NPV analysis as a function of the discount rate and cost of rehandling implementing SIMSCHED DBS to open pit mining

Truck-shovel allocation optimisation: a goal programming approach

Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines

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