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Record W2264919365

An Enhanced Scheduling Technique for Modular Construction Manufacturing

2014· article· en· W2264919365 on OpenAlexaff
Mana Moghadam, Mohamed Al‐Hussein

Bibliographic record

Venue22nd Annual Conference of the International Group for Lean Construction · 2014
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsModular designLean manufacturingManufacturing engineeringAdvanced manufacturingLean project managementManufacturingScheduling (production processes)EngineeringComputer scienceOperations managementBusiness
DOInot available

Abstract

fetched live from OpenAlex

Modular construction manufacturing (MCM) is potentially built through a more efficient and cost-effective method compared to the on-site construction practice. The increased interest in manufacturing of the building construction process demands special methods of design and manufacturing to improve production efficiency. MCM provides opportunities to apply Lean for production efficiency in the plant, including eliminating waste and supporting the delivery of customized products in a shorter time and at a lower cost. Lean is a concept first developed in the manufacturing industry which has been since adapted to the construction industry. Although the focus of Lean in both industries is the same, Lean tools vary between manufacturing and construction since these two industries differ in nature. Lean as the concept is applicable to any industries, taking into consideration that MCM has characteristics of both manufacturing and construction yet is distinct and should be seen in the class of its own. Given the distinct nature of MCM, the technical elements in “Lean production” and “Lean construction” are not sufficient to achieve the Lean goals for MCM industry, necessitating a modified framework by which to exploit the potential benefits of modular building. This paper provides a deeper understanding of the modular construction manufacturing and the difference between the manufacturing, construction, and MCM industries. The focus of this paper is to adopt an enhanced scheduling technique which can adequately fulfill the production efficiency demands based on particular characteristic of modular construction manufacturing.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.580
Threshold uncertainty score0.699

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 head0.010
GPT teacher head0.225
Teacher spread0.215 · 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 designBench or experimental
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

Citations2
Published2014
Admission routes1
Has abstractyes

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