Real-time decision support for planning concrete plant operations enabled by integrating vehicle tracking technology, simulation, and optimization algorithms
Bibliographic record
Abstract
By integrating the vehicle tracking system, discrete-event simulation algorithm, and evolutionary optimization algorithm, we developed HKCONSIM-Realtime, a decision-support platform created specifically for handling ready-mixed concrete operations. This platform is capable of (1) tracking the positions of concrete trucks and monitoring the motion and status of concrete deliveries in real time, (2) transforming the tracking records into data that provide updated input to simulation, and (3) optimizing the operations and logistics of concrete production based on simulation of the production system using the most current data. This paper presents an overview of the design and development of (1) the hardware and software modules, (2) the data flow and processing throughout the system, and (3) the role of the system in providing interactive, effective support for the human operator to attain cost efficiency. Case studies are given to demonstrate the functionality and application of the prototype system. Key words: simulation, optimization, vehicle tracking, construction planning, ready-mixed concrete, Hong Kong.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".