Solving LTPSOP in open-pit mines using Gaussian process and human mental search

Kamyar Tolouei, Ehsan Moosavi () and Mehran Gholinejad
Additional contact information
Kamyar Tolouei: Islamic Azad University
Ehsan Moosavi: Islamic Azad University
Mehran Gholinejad: Islamic Azad University

OPSEARCH, 2024, vol. 61, issue 3, No 3, 1092 pages

Abstract: Abstract In this paper, to synthesize grade uncertainty into the strategic mine schedule, a stochastic mixed-integer programming framework (SMIP) is presented for the long-term production scheduling optimization problem (LTPSOP) in open-pit mines. The objective function maximizes the net present value and minimizes the risk of deviation from the production targets considering grade uncertainty simultaneously while satisfying all technical constraints and operational requirements. The current study has proposed a hybrid model to solve the LTPSOP with the incorporation of augmented Lagrangian relaxation (ALR) and the human mental search metaheuristic algorithm (HMS). The ALR function is formulated and the HMS is used to update the Lagrange multipliers. Besides, a Gaussian process machine learning method is applied to estimate the grade in a mineral deposit. The results obtained from a supercomputer have indicated that the ALR–HMS method conducted on real case studies generates 18.29% and 31.56% more cumulative net present value; 9.75% and 13.98% larger pit size than the conventional method, respectively. Finally, while improving the solvability and optimality gap, the analysis of the outcomes has demonstrated that the proposed model yields a near-optimal solution with a rational time.

Keywords: Open-pit mine; Production scheduling optimization; Grade uncertainty; Metaheuristic algorithms; Machine learning; Lagrangian relaxation (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s12597-024-00744-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:opsear:v:61:y:2024:i:3:d:10.1007_s12597-024-00744-6

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/12597

DOI: 10.1007/s12597-024-00744-6

Access Statistics for this article

OPSEARCH is currently edited by Birendra Mandal

More articles in OPSEARCH from Springer, Operational Research Society of India
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().