Snowplough service area reconfiguration using workload balancing techniques with route optimisation for large municipalities
Bibliographic record
Abstract
Snowplowing is a complex, expensive, and mandatory winter fleet operation that benefits municipalities worldwide. In this research, three clustering approaches were used to create new snowplough route configurations for the City of Surrey, Canada, and the Smart Selective Navigator (SSN) method was used to optimise the routes. The three clustering approaches used are the current configuration-based dynamic clustering, static and dynamic clustering, and static and dynamic clustering with depot-to-cluster distance. The first clustering approach uses the existing configuration as a start point and makes minor changes, while the others generate new clusters from scratch with an objective of improving the workload distribution. SSN is a turn-based route optimisation algorithm that was improved by adding advanced turn-tracking methods capable of generating feasible routes in complex geographic information system (GIS) road network data. The simulation results show improvements when high-priority roads are clustered using the minor modification approach, and lower-priority roads are clustered from scratch. Overall, the clustering approaches can save 51 min of simulated travel time while significantly improving the workload balance.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".