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Record W52441173 · doi:10.22260/isarc2014/0036

Post-Simulation Visualization Application for Production Improvement of Modular Construction Manufacturing

2014· article· en· W52441173 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueProceedings of the ... ISARC · 2014
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsModular designVisualizationComputer scienceContext (archaeology)Process (computing)Lean manufacturingDiscrete event simulationProduction (economics)Material flowProduction planningIndustrial engineeringManufacturing engineeringSimulationEngineeringData mining

Abstract

fetched live from OpenAlex

Post-Simulation Visualization Application for Production Improvement of Modular Construction Manufacturing M. Moghadam, B. Barkokebas, M. Al-Hussein Pages 270-277 (2014 Proceedings of the 31st ISARC, Sydney, Australia, ISBN 978-0-646-59711-9, ISSN 2413-5844) Abstract: The modular construction manufacturing (MCM) process is a complex operation that combines line flow product movement with a complex activity precedence network. There are physical constraints related to the given facility, as well as logical constraints caused by demand variation. In order to change production line layout and make improvements within the context of Lean, a tool is needed to assist MCM to quantify, at the planning and evaluation stages, the benefits they can expect from proposed changes to their system. Simulation is a technique by which to facilitate identifying changes and benefits of future transformation and to determine where valuable resources should be applied prior to actual implementation. Despite the benefits of simulation, project management teams typically are unwilling to make decisions based on current simulation outputs, since they are very difficult to understand and require specialized skills in interpretation of the information. Visualization is a more popular technique since it fosters better understanding of the construction process. However, to be effective for decision making purposes, such a model must be linked to project information. The visual interpretation of the simulation results constitutes a more effective approach. In this paper, a simulation model is thus generated to provide results for different production scenarios, and then the near-optimum scenarios are run to visualize the production constraints, which facilitates precise scenario comparison. The developed model capitalizes on the advantages of both simulation and visualization, whereby critical information such as the 3D model, time constraints, and resource demand are incorporated into the system. The proposed methodology is validated by a case study, a residential modular factory in Edmonton, Canada, which illustrates the effectiveness of the proposed methodology. Keywords: Modular Construction Manufacturing, Lean, Production, Simulation, Visualization, Resource Utilization DOI: https://doi.org/10.22260/ISARC2014/0036 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.356

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 head0.211
Teacher spread0.205 · 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