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Record W3097564098 · doi:10.3390/su12219266

Collaborative Scheduling of On-Site and Off-Site Operations in Prefabrication

2020· article· en· W3097564098 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

VenueSustainability · 2020
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPrefabricationScheduling (production processes)EngineeringSimulated annealingConstruction managementTransport engineeringComputer scienceOperations researchOperations managementCivil engineering

Abstract

fetched live from OpenAlex

As a kind of sustainable technology, prefabricated construction has increasingly gained momentum internationally due to its numerous benefits that include, but are not limited to, safe construction, waste minimization, quality improvement, and productivity enhancement. However, productivity in this domain is reliant on the efficiency of both on-site and off-site operations. On this basis, focusing on collaborative scheduling mechanisms, the current paper develops a static scheduling model and a dynamic scheduling model in prefabricated construction, and uses a simulated annealing algorithm (SA) to settle the optimization of operation planning considering delays by risks. The developed models are validated using data from a construction project with multiple suppliers of prefabricated elements. This study contributes to the body of knowledge in prefabricated construction management by streamlining collaborative scheduling in prefabrication. The established models provide construction managers with decision support systems with the aims of minimizing delays and related cost overruns.

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 score0.121
Threshold uncertainty score0.267

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 head0.005
GPT teacher head0.225
Teacher spread0.220 · 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