Integration of Uncertain Real-Time Logistics Data for Reactive Scheduling Using Fuzzy Set Theory
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".