eprintid: 5679 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/00/56/79 datestamp: 2023-02-02 23:30:06 lastmod: 2023-07-11 23:31:19 status_changed: 2023-02-02 23:30:06 type: article metadata_visibility: show creators_name: Joshi, Devendra creators_name: Ali Albahar, Marwan creators_name: Chithaluru, Premkumar creators_name: Singh, Aman creators_name: Yadav, Arvind creators_name: Miró Vera, Yini Airet creators_id: creators_id: creators_id: creators_id: aman.singh@uneatlantico.es creators_id: creators_id: yini.miro@uneatlantico.es title: A Novel Approach to Integrating Uncertainty into a Push Re-Label Network Flow Algorithm for Pit Optimization ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica full_text_status: public keywords: open-pit mine; ultimate pit limit; uncertainty modeling; minimum cut; network flow abstract: The standard optimization of open-pit mine design and production scheduling, which is impacted by a variety of factors, is an essential part of mining activities. The metal uncertainty, which is connected to supply uncertainty, is a crucial component in optimization. To address uncertainties regarding the economic value of mining blocks and the general problem of mine design optimization, a minimum-cut network flow algorithm is employed to give the optimal ultimate pit limits and pushback designs under uncertainty. A structure that is computationally effective and can manage the joint presentation and treatment of the economic values of mining blocks under various circumstances is created by the push re-label minimum-cut technique. In this study, the algorithm is put to the test using a copper deposit and shows similarities to other stochastic optimizers for mine planning that have already been created. Higher possibilities of reaching predicted production targets are created by the algorithm’s earlier selection of more certain blocks with blocks of high value. Results show that, in comparison to a conventional approach using the same algorithm, the cumulative metal output is larger when the uncertainty in the metal content is taken into consideration. There is also an additional 10% gain in net present value. date: 2022-12 publication: Mathematics volume: 10 number: 24 pagerange: 4803 id_number: doi:10.3390/math10244803 refereed: TRUE issn: 2227-7390 official_url: http://doi.org/10.3390/math10244803 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica Abierto Inglés The standard optimization of open-pit mine design and production scheduling, which is impacted by a variety of factors, is an essential part of mining activities. The metal uncertainty, which is connected to supply uncertainty, is a crucial component in optimization. To address uncertainties regarding the economic value of mining blocks and the general problem of mine design optimization, a minimum-cut network flow algorithm is employed to give the optimal ultimate pit limits and pushback designs under uncertainty. A structure that is computationally effective and can manage the joint presentation and treatment of the economic values of mining blocks under various circumstances is created by the push re-label minimum-cut technique. In this study, the algorithm is put to the test using a copper deposit and shows similarities to other stochastic optimizers for mine planning that have already been created. Higher possibilities of reaching predicted production targets are created by the algorithm’s earlier selection of more certain blocks with blocks of high value. Results show that, in comparison to a conventional approach using the same algorithm, the cumulative metal output is larger when the uncertainty in the metal content is taken into consideration. There is also an additional 10% gain in net present value. metadata Joshi, Devendra; Ali Albahar, Marwan; Chithaluru, Premkumar; Singh, Aman; Yadav, Arvind y Miró Vera, Yini Airet mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, yini.miro@uneatlantico.es (2022) A Novel Approach to Integrating Uncertainty into a Push Re-Label Network Flow Algorithm for Pit Optimization. Mathematics, 10 (24). p. 4803. ISSN 2227-7390 document_url: http://repositorio.uneatlantico.es/id/eprint/5679/1/mathematics-10-04803-v2.pdf