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Record W157466560 · doi:10.22260/isarc2013/0076

Integration of Uncertain Real-Time Logistics Data for Reactive Scheduling Using Fuzzy Set Theory

2013· article· en· W157466560 on OpenAlexaboutno aff
Kamil Szczesny, Markus König, Lars Laußat, Manfred Helmus

Bibliographic record

VenueProceedings of the ... ISARC · 2013
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceScheduling (production processes)ScheduleFuzzy logicFuzzy setOperations researchDiscrete event simulationUncertain dataReal-time computingData miningIndustrial engineeringMathematical optimizationSimulationArtificial intelligenceEngineeringMathematics

Abstract

fetched live from OpenAlex

Integration of Uncertain Real-Time Logistics Data for Reactive Scheduling Using Fuzzy Set Theory K. Szczesny, M. König, L. Laußat, M. Helmus Pages 691-698 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: This paper considers the integration of uncertain real-time logistics data for reactive construction scheduling. In order to manage a construction project efficiently, an accurate schedule representing the current project progress is inevitable. The quality and up-to-dateness of such a schedule depends on the availability of real-time data. Typically, real-time logistics data contain information about the availability of material, equipment and personnel as well as delivery dates and site conditions. The accuracy and inherent uncertainty depends on the location where the real-time data was acquired. Currently, the integration of such data into a construction schedule is a very time-consuming, manual and, thus, errorprone process. Therefore, this paper proposes a methodology that enables an automatic integration of such uncertain data into construction schedules. By integrating uncertainties into the existing schedule their impacts on the construction work can be evaluated. For this, discrete event simulation is applied. In order to model uncertain input parameters for simulation models this methodology applies the fuzzy sets theory. In combination with alpha-cut sampling technique, discrete model input parameters are obtained. By applying reactive scheduling with several discrete event simulation experiments, the results can be used to modify construction schedules according to agreed timeframes and costs. In order to demonstrate and validate the presented approach an example is conducted. Keywords: Real-time logistics data, reactive construction scheduling, uncertainty modeling, fuzzy set theory, discrete event simulation DOI: https://doi.org/10.22260/ISARC2013/0076 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.050
GPT teacher head0.275
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... ISARCSame topicBIM and Construction IntegrationFrench-language works237,207