Waste Collection Area Generation Using a 2 Stage Cluster Optimization Process and GIS Data
Bibliographic record
Abstract
The division of a city into several waste collection areas can have a large influence on the workload distribution put in place for the waste collection personnel. Additionally, areas with rapid urban development can benefit from a systematic way to automate the collection area division given that there is geographical information containing stop locations with an approximate number of dwelling units. This paper proposes a 2-stage collection area optimization process using the weighted K-means algorithm paired with differential evolution to minimize the standard deviation of dwelling units across each collection area. Results from a case study in The City of Oshawa, Canada prove that the proposed clustering techniques can yield a set of collection areas with 87.75% improvement compared to the current arrangement in terms of balancing the dwelling units. Additionally, the same clustering techniques can be used to assign collection routes for the vehicles in each area. A combination of Dijkstra’s and Hierholzer’s algorithms is applied to generate a route simulation with accompanying statistics regarding the total distance travelled, collection time, travel time, and fuel consumed. Specific to the case study in The City of Oshawa, each day of the week has 2 collection areas, and a genetic algorithm is used to find the optimal collection area pairs. Results from the collection area pairing show that there is a 38.04% and 37.54% improvement of simulated statistics for Week 1 and 2 collections, respectively.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".