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Record W4399126077 · doi:10.18280/ijsdp.190504

Sustainable Transportation Planning for Djebel Onk's Open-Pit Phosphate Mine in Algeria: Using Queuing Theory and Cost-Emission Analysis

2024· article· en· W4399126077 on OpenAlexvenueno aff
Mokhtar Debbouz, Zoubir Aoulmi, Chamseddine Fehdi, Moussa Attia

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsQueueing theoryEnvironmental scienceSustainable developmentMining engineeringEnvironmental planningGeologyComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

Efficient mineral transportation is critical for sustainable open-cast mining operations.Queuing theory offers a practical approach to optimizing truck-shovel systems and address truck waiting times at loading and unloading sites.This research evaluates the excavator-truck system at an Algerian open-pit phosphate mine using an M/M/1 queuing model.The model reveals relationships between truck fleet size and queue length, waiting time, shovel utilization, and overall production.Moreover, loading and transportation costs are analysed to determine the optimal truck fleet size that minimizes costs and emissions.The match factor further evaluates fleet compatibility for sustainable planning.This systems analysis provides insights into achieving efficient, low-emission truck-shovel operations through optimized fleet sizes, reduced waiting times, and cost-emission optimization.The integrated queuing model and planning techniques presented can guide sustainable planning of open-pit mining transportation systems.Focusing on efficiency, costs, and emissions allows strategic optimization for both economic and environmental sustainability.

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 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)0.001
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 score0.125
Threshold uncertainty score0.592

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.019
GPT teacher head0.292
Teacher spread0.272 · 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 teacher head, 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

Citations3
Published2024
Admission routes1
Has abstractyes

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