High-resolution terrain models of open-pit mine highwalls and benches are essential in developing new automated slope monitoring systems for operational optimization. This paper presents several contributions to the field of remote sensing in surface mines providing a practical framework for generating high-resolution images using low-trim Unmanned Aerial Vehicles (UAVs). First, a novel mobile application was developed for autonomous drone flights to follow mine terrain and capture high-resolution images of the mine surface. In this article, case study is presented showcasing the ability of developed software to import area terrain, plan the flight accordingly, and finally execute the area mapping mission autonomously. Next, to model the drone’s battery performance, empirical studies were conducted considering various flight scenarios. A multivariate linear regression model for drone power consumption was derived from experimental data. The model has also been validated using data from a test flight. Finally, a genetic algorithm for solving the problem of flight planning and optimization has been employed. The developed power consumption model was used as the fitness function in the genetic algorithm. The designed algorithm was then validated using simulation studies. It is shown that the offered path optimization can reduce the time and energy of high-resolution imagery missions by over 50%. The current work provides a practical framework for stability monitoring of open-pit highwalls while achieving required energy optimization and imagery performance.

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Recent research showing theoretical generative models for heavy-tailed service time queues and its empirical validation implies the need for a better knowledge of the key performance indicators' behaviour under such assumption. The behaviour of the average length of the queue (Lp) and the average waiting-time (Wp)were analysed through simulation, varying system capacity, average service utilisation factor (r) and the number of servers in the systems as parameters. Comparisons were also made with service times based on Poisson processes. The results showed more sensitive variations Lq and Wq heavy-tailed service times than for Poisson-based service times. Systems having a capacity of over 1,000 entities might be considered as being systems having infinity capacity and the number of servers has a greater importance in heavy-tailed ruled processes than in Poisson processes. There was a lack adequacy Lq and Wq as key performance indicators for heavy-tailed service times, leading to unexpected and unstable results.

El problema de la agregación o desagregación de series temporales para la realización de pronósticos se presenta frecuentemente en situaciones empresariales y econométricas. Este trabajo presenta una metodología novedosa para la selección de un nivel de desagregación adecuado de las series temporales a partir del cual realizar pronósticos. La metodología toma en cuenta criterios cualitativos -los recursos empresariales y el entorno de decisión- y cuantitativos -predictibilidad de las series y calidad de la información-, utilizando la metodología de toma de decisiones multicriterio conocida como el proceso analítico jerárquico (AHP) para llegar a una decisión final. Un caso de estudio en una empresa de suscripción muestra la utilidad de combinar AHP con técnicas de pronóstico de series de tiempo y la importancia de utilizar múltiples criterios en la selección de un nivel de desagregación adecuado.