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Record W2098362641 · doi:10.1139/l10-071

Simulation-based aggregate planning of batch plant operations

2010· article· en· W2098362641 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.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicAssembly Line Balancing Optimization
Canadian institutionsNorth American Construction Group (Canada)Canadian Natural Resources
Fundersnot available
KeywordsProduction (economics)Aggregate planningAggregate (composite)Supply chainBatch productionKey (lock)AsphaltProduction planningTask (project management)Service levelService (business)Computer scienceEngineeringCivil engineeringOperations researchOperations managementSystems engineeringBusinessEconomics

Abstract

fetched live from OpenAlex

Production and supply of construction materials plays a significant role in the delivery of constructed facilities, especially for concrete and asphalt batch plants. The construction material (e.g., concrete) supply chain presents unique challenges, but is a key factor in successfully delivering facilities. This paper presents the development and application of a simulation-based aggregate planning approach that facilitates modeling and coordination of a batch plant’s supply chain. The tool is applied to a real case of asphalt production operations, where fluctuating demand affects the service level of the production plant and makes the planning of production and inventory processes a challenging task. The model quantifies the effects of different parameters of the asphalt production plant on its level of service and assists in finding the best configurations for the plant’s production, inventory, and distribution processes.

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 score0.627
Threshold uncertainty score0.993

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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