Reduction of queuing delays at waste management facilities
Bibliographic record
Abstract
ile waiting to unload materials at waste management facilities such as landfill sites, transfer stations, and material recovery facilities. These delays can be costly since the program operator must pay for these trucks and their crews to sit idly. Previous studies of delays at unloading facilities have often focussed on reducing unloading times, primarily through capital improvements such as providing twin scale houses and additional unloading bays. Most of these studies assume that the "arrival pattern of the collection vehicles is beyond the control of the analyst. This work assumes that the physical layout of the unloading facility is fixed and examines the effect that changes in the arrival times of collection vehicles will have on queuing delays at the facility. Both deterministic and fluid flow approaches to the analysis of queuing delays at unloading facilities are presented. The results show that congestion at unloading facilities is often caused by the assignment of approximately equal workloads to each collection crew and that relatively minor differences in workload assignments can substantially reduce queuing delays. The results of the analysis are confirmed through Monte Carlo simulation modelling.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".