MétaCan
Menu
Back to cohort
Record W4313422163 · doi:10.24867/ijiem-2022-4-316

Optimized Buffer Allocation and Repair Strategies for Series Production Lines

2022· article· en· W4313422163 on OpenAlex

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 Industrial Engineering and Management · 2022
Typearticle
Languageen
FieldEngineering
TopicScheduling and Optimization Algorithms
Canadian institutionsUniversité LavalUniversité de Moncton
Fundersnot available
KeywordsThroughputProduction (economics)Production lineComputer scienceProcess (computing)HeuristicReliability engineeringQuality (philosophy)Operations researchMathematical optimizationEngineering

Abstract

fetched live from OpenAlex

The industrial entities continuously urge for innovative methodologies to survive the competition and maximize the throughput of production systems. Further, the recent advances in technology operations and market fluctuations imposed additional challenges to the operation of the production systems. To address the aforementioned challenges, the production operations need to be customized to control the costs, especially those related to equipment allocations, semi-finished products handling, and personnel circulations. This will definitely contribute to increasing the performance and effectiveness of the production systems. Therefore, reliable and flexible production methods are required to maintain a high-level of product quality, optimize and rationalize the use of equipment, and reduce the design and operating costs of the overall production process.

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: none
Teacher disagreement score0.502
Threshold uncertainty score0.327

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 head0.231
Teacher spread0.211 · 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