RESEARCH ARTICLE


A Genetic Algorithm with Zooming for the Determination of the Optimal Open Pit Mines Layout



Gabriele Milani*
Department of Architecture, Built environment and Construction engineering ABC Politecnico di Milano Piazza Leonardo da Vinci 32, 20133 Milan, Italy


Article Metrics

CrossRef Citations:
0
Total Statistics:

Full-Text HTML Views: 3866
Abstract HTML Views: 1879
PDF Downloads: 800
ePub Downloads: 520
Total Views/Downloads: 7065
Unique Statistics:

Full-Text HTML Views: 1649
Abstract HTML Views: 1064
PDF Downloads: 548
ePub Downloads: 320
Total Views/Downloads: 3581



Creative Commons License
© Gabriele Milani; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Department of Architecture, Built environment and Construction engineering ABC Politecnico di Milano Piazza Leonardo da Vinci 32, 20133 Milan, Italy; Tel: +39 022399 4225; Fax: +39 022399 4220; E-mail: gabriele.milani@polimi.it


Abstract

A Genetic Algorithm (GA) with nested zooming strategy is proposed for the determination of the optimal open pit mine design.

Different genetic procedures are applied to increase robustness, namely two typologies of admissible mutations for the elite sub-population subjected to zooming and mutation and reproduction for the remaining individuals. In order to further improve convergence rate, a user-defined population percentage, depending on individuals fitness, is replaced with new phenotypes, enforcing chromosomic renewal.

Several comparisons with (traditionally used) dynamic programming approaches are provided both for 2D and 3D open pit mines. Both small and large scale mines are analyzed, to benchmark the code in presence of several variables.

Results show that the procedure proposed requires a very limited computational effort, both for challenging problems with several variables and when a micro-GA (populations with few individuals) is adopted for small scale problems.

Keywords: 2D and 3D numerical simulations, Economic value maximization, Genetic Algorithm, Integer programming, Open pit mine design.