Mathematical model for optimising the sequence for clearing snow from the manoeuvring area during winter operations
Bibliographic record
Abstract
Summary This article considers the optimisation of the sequence for clearing snow from stretches of the manoeuvring area of an airport. This issue involves the optimisation of limited resources to remove snow from taxiways and runways thereby leaving them in an acceptable condition for operating aircraft. The airfield is divided into subsets of significant stretches for the purpose of operations and target times are established during which these are open to aircraft traffic. The document contains several mathematical models each with different functions, such as the end time of the process, the sum of the end times of each stretch and gap between the estimated and the real end times. During this process, we introduce different operating restrictions on partial fulfilment of the operational targets as applied to zones of special interest, or relating to the operation of the snow‐clearing machines. The problem is solved by optimisation based on linear programming. The article gives the results of the computational tests carried out on five distinct models of the manoeuvring area, which cover increasingly complex situations and larger areas. The mathematical model is particularised for the case of the manoeuvring area of Adolfo Suarez Madrid—Barajas Airport. Copyright © 2016 John Wiley & Sons, Ltd. Highlights Optimal sequence for clearing snow from the manoeuvring area of an airport. Contains optimising algorithms solved using CPLEX LP‐based tree search. Restrictions on partial fulfilment of operational targets applied to subsets of significant stretches, used for planning the operation of snow‐clearing machines. Model applied to the case of the manoeuvring area of Adolfo Suárez Madrid Barajas Airport. Conclusions are given on the results of the computational tests carried out. There are five models of the manoeuvring area which cover increasingly complex situations and larger areas.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 0.001 |
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".