Vehicle Routing for Urban Snow Plowing Operations
Bibliographic record
Abstract
Winter road maintenance planning involves a variety of decisions related to the routing of vehicles for spreading chemicals and abrasives, plowing roadways and sidewalks, loading snow into trucks, and transporting snow to disposal sites. In this paper, we present a model and two heuristic solution approaches based on mathematical optimization for the routing of vehicles for snow plowing operations in urban areas. Given a district and a single depot where a number of plows are based, the problem is to determine a set of routes, each performed by a single vehicle that starts and ends at the district's depot, such that all road segments are serviced while satisfying a set of operational constraints and minimizing a time objective. The formulation models general precedence relation constraints with no assumption on class connectivity, different service and deadhead speed possibilities, separate pass requirements for multilane road segments, class upgrading possibilities, and vehicle road segment dependencies. Several extensions, such as turn restrictions, load balancing constraints, and tandem service requirements, which are required in a real-life application, are also discussed. Two objectives are considered: A hierarchical objective and a makespan objective. The resulting model is based on a multicommodity network flow structure to impose the connectivity of the route performed by each vehicle. The two solution strategies were tested on data from the City of Dieppe, New Brunswick, Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".