A genetic algorithm to solve one mining selection problem

Based on the decision to automate the solution of an optimization problem related to the selection of an "N" number of geological exploration wells in a reservoir, which reflects the general characteristics of the well and having the mathematical models created for that purpose, arises the need to look for some alternative that allows to obtain the solutions of the raised problem. The mathematical models created generate a system formed by an objective function, several constraints in the form of inequalities and an additional restriction, in the form of equality: a classic problem of selection. This type of problem has solutions of non-polynomial computational complexity; for this reason, instead of implementing complex algorithms, it was decided to design an automated tool based on the use of genetic algorithms. An automated application was created for the solution of the problem of selection of a described mining sample, being able toimplement a low complexity computational solution.A partir de la decisión de automatizar la solución de un problema de optimización vinculado a la selección de una cantidad “N” de pozos de un yacimiento, que refleje las características generales estudiadas del mismo y disponiendo de los modelos matemáticos creados a tal efecto, surge la necesidad de buscar alguna alternativa que permita obtener las soluciones del problema planteado. Los modelos matemáticos creados generan un sistema formado por una función objetivo, varias restricciones en forma de desigualdades y una restricción adicional, en forma de igualdad: un problema clásico de selección. Este tipo de problema tiene soluciones de complejidad computacional no polinomial; por tal motivo, en lugar de implementar algoritmos complejos, se decidió diseñar una herramienta automatizada basada en el empleo de algoritmos genéticos. Se creó una aplicación automatizada para la solución del problema de selección de una muestra minera descrito, lográndose implementar una solución computacional de baja complejidad

Técnicas genéticas en la solución de un problema minero

A mathematical model’s system for optimizing the efficiency for sampling exploration and exploitation networks in lateritic nickel and cobalt deposits, located in the north-eastern province of Holguin, was developed in the “Centro de Investigaciones del Níquel”. This system includes a new reservoir model based on multivariate substantial classification using the Markov model for discrete stochastic processes. As a result of the application of these models a linear optimization problem was obtained comprising an objective function, several constraints as inequalities and an additional restriction in the form of equality linked to the number of wells to be selected. The generated problem has polynomial computational complexity and because no accurate methods exist to solve it, an automated tool that brings up feasible solutions was developed based on genetic algorithms