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In this paper, the flashlight (FL) algorithm, which is categorized as a heuristic method, has been suggested to determine the ultimate pit limit (UPL). In order to apply the suggested algorithm and other common algorithms, such as the dynamic programming, the Korobov, and the floating cone, and to validate the capability of the proposed method, the...

## Contexts in source publication

**Context 1**

... mining of each block requires the extracting of a cone from other blocks on top of the block, and the block can place within the optimal limit only when its mining can help improve the final value and does not reduce the value. Generally, the decision should be made according to the flowchart shown in figure 1 for the feasibility study of block mining. The FL algorithm considers that mining a positive block and its upper cone is only economical when it ultimately improves the solution. ...

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## Citations

... A hypothetical flashlight is applied, in which all blocks that were illuminated determine the ultimate pit limit for an iteration, then the positive blocks are evaluated and placed on a blocklist or in a whitelist, the first consisting of blocks that must be forgotten, while the second groups the blocks that can be in the main or replaced flashlights. An application in a cross section of an iron ore deposit showed that the flashlight heuristic reached the same value as a rigorous approach (the dynamic programming algorithm discussed below), obtaining great improvements compared to the floating cone and Korobov's algorithms [17,[28][29][30][31][32]. ...

Planning is one of the fundamental tasks for surface mine projects, and it can play a critical role in the success and results obtained after the development and operationalization of the project. This role will be more important when the miners need to go deeper to access the ore, especially when its grade tends to decrease. The quality of the surface mine planning decision depends on the available data, information, and experience of the decision makers, being imperative that robust decision criteria, new technologies and advanced analytics tools are employed in order to make the most of the huge amount of data generated at each instant. This chapter focuses on advanced analytics approaches such as machine learning and artificial intelligence as valuable decision-making tools to improve the surface mine planning process, as well as mathematical optimization techniques already successfully employed in solving ultimate pit and production scheduling problems, using rigorous algorithms, heuristics and metaheuristics. This chapter also covers stochastic mine planning and discusses future mine planning.

This work attempts to estimate the amount of fly-rock in the Angoran mine in the Zanjan province (Iran) using the gene expression programming (GEP) predictive technique. For this, the input data including the fly-rock, mean depth of the hole, powder factor, stemming, explosive weight, number of holes, and booster is collected from the mine. Then using GEP, a series of intelligent equations are proposed in order to predict the fly-rock distance. The best GEP equation is selected based on some well-established statistical indices in the next stage. The coefficient of determination for the training and testing datasets of the GEP equation are 0.890 and 0.798, respectively. The model obtained from the GEP method is then optimized using the teaching-learning-based optimization (TLBO) algorithm. Based on the results obtained, the correlation coefficient of the training and testing data increase to 91% and 89%, which increases the accuracy of the equation. This new intelligent equation could forecast fly-rock resulting from mine blasting with a high level of accuracy. The capabilities of this intelligent technique could be further extended to the other blasting environmental issues. Keywords Blasting operations Fly-rock Gene expression programming Teaching-learning-based optimization algorithm