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Record W3215528506 · doi:10.1080/17480930.2021.1996983

Optimisation of open-pit mine production scheduling considering optimum transportation system between truck haulage and semi-mobile in-pit crushing and conveying

2021· article· en· W3215528506 on OpenAlex
Mohammad Shamsi, Yashar Pourrahimian, Mehdi Rahmanpour

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Mining Reclamation and Environment · 2021
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHaulageTruckCrusherShovelOpen-pit miningScheduleEngineeringRelocationNet present valueScheduling (production processes)Production (economics)Transport engineeringOperations managementMining engineeringComputer scienceAutomotive engineeringRope

Abstract

fetched live from OpenAlex

Despite the upward trend in using semi-mobile in-pit crushing and conveying system (SMIPCC), some mining companies still hesitate to use SMIPCC in their operations. Therefore, a framework is needed to facilitate decision-making between SMIPCC and truck and shovel system (TS) and satisfy the location and relocation time of the semi-mobile crusher (SMC). For this purpose, this paper presents a mathematical integer programming (IP) model considering the operational constraints. The model specifies both the optimum location and optimum relocation time of SMC. Also, it compares the production schedule for the TS and SMIPCC utilised for ore and waste handling over the life of mine. Then, the system with maximum net present value (NPV) is selected as the optimal material handling system. Finally, the model is implemented with a case study. The results show that implementing SMIPCC instead of the TS system for ore and waste materials improves the NPV by 69.77%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.239
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it