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Record W3169202935 · doi:10.29173/mocs152

Workspace Optimization for Modular Off-site Assembly

2015· article· en· W3169202935 on OpenAlexvenueno aff
Hosang Hyun, Hyunsoo Lee, Moonseo Park, Jeoung-Hoon Lee, Min-Jung Kim

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsnot available
FundersMinistry of Land, Infrastructure and Transport
KeywordsModular designWorkspaceFactory (object-oriented programming)Process (computing)Unit (ring theory)Production lineManufacturing engineeringConstraint (computer-aided design)Computer scienceEngineeringProduction (economics)Object (grammar)Industrial engineeringOperations managementMechanical engineeringArtificial intelligenceMathematicsOperating system

Abstract

fetched live from OpenAlex

Modular construction is getting more prevalent than the past because it is environmentallyfriendly construction method and it can reduce construction period and cost by manufacturing in factory. Modular unit is produced through assembly line where the intensive manufacturing process is conducted. The manufacturing process is a complicated operation that integrates the production movement with a complex activity precedence network (procedure). Although modular unit production is a sum of the general construction activity superimposed on a production line in modular factory, the activities should be conducted on station for the optimal cycle time and many activities are carried out in the modular unit. So there is spatial constraint in the manufacturing process and modular construction manager suffers from existing limitations such as space, workers, materials which are caused by intensive process. If the manager doesn’t consider the workspace interference when establishing construction process, the efficiency of modular construction would be reduced. The object of this research is to suggest the method which optimize the number of workers for activities on the station and increase the productivity by inputting appropriate number of workers for minimum workspace interference.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.436
Threshold uncertainty score1.000

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 communication0.0000.001
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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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