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Record W7115034188

Capacity Planning Optimization Integrating Cut-Off Grade and Block Sequencing Under Economies of Scale, Cost Structures, and Grade Distribution Considerations

2025· dissertation· en· W7115034188 on OpenAlexafffund

Bibliographic record

VenueeScholarship@McGill (McGill) · 2025
Typedissertation
Languageen
FieldDecision Sciences
TopicResource-Constrained Project Scheduling
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCapacity planningBlock (permutation group theory)Distribution (mathematics)Economies of scaleSequence (biology)
DOInot available

Abstract

fetched live from OpenAlex

Mine planning process has three major problems: (1) selection/determination of mining and processing capacities/rates, (2) cut-off grade(s), and (3) block sequencing.Currently, these problems are solved sequentially because of the large size of the problem.The sequential approach might undervalue projects.Despite significant knowledge accumulation on cut-off grade and block sequencing, capacity selection has usually been overlooked.In current practice, the capacity based on the financial resources of the investor is mainly used.This capacity selection method ignores:(i) qualitative/quantitative heterogeneity within the mineral deposit, (ii) the interdependencies between the problems (insoluble conundrum or circular loop), (iii) the effect of the economies of scale, (iv) the relationship between capacity and innovation, and (v) the relationship between mining and mineral processing capacities.As a result, mining operations frequently encounter under-capacity/over-capacity issues, resulting in profit losses.This thesis focuses on optimizing capacity planning by exploring its relationships with cut-off grade and block sequencing.Economies of scale represent a significant phenomenon that complicates capacity planning, especially when multiple interdependent capacities are involved.Rapid technological innovations add further complexity to capacity planning.In this context, the innovative characteristics of the mineral industries are examined in relation to capacity and economies of scale.Access constraints First and foremost, I would like to extend my deepest gratitude to my supervisor, Professor Mustafa Kumral, for his exceptional guidance, immense knowledge, and unwavering patience.Despite his demanding schedule, he consistently took the time to warmly welcome me and clarify the critical concepts and aspects of my research.This Ph.D. study would not have been possible

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.081
GPT teacher head0.324
Teacher spread0.243 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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