“That’s (not) Rubbish!” Planning the Reverse Logistic for Recyclable Solid Waste Using a Vehicle Routing Problem: A Case Study in Vitória/ES
Bibliographic record
Abstract
The rapid growth of urban populations has led to a significant increase in municipal solid waste generation, raising concerns about waste disposal, environmental impact, and associated costs.This paper aims to plan a strategy for the reverse logistics of selective waste collection in a medium-sized city in Brazil to achieve more effective and economical waste collection.Firstly, the methodology of the Brazilian National Foundation of Health was applied to determine the fleet of vehicles needed to collect recyclable waste from Volunteer Delivery Stations.Subsequently, a Vehicle Routing Problem with Multiple Trips (VRPMT) was proposed as a case study for the city of Vitória, Brazil.This study represents the first application of VRPMT to a medium-sized city with geographically dispersed collection points, introducing multiple routes within a single day and presenting a paradigm shift.The results obtained from the mathematical model indicate a potential route savings of 48.7% using linear programming analysis compared to the existing municipal solid waste collection planning.The model can serve as a tool to support the planning of reverse logistics for recyclable solid waste, assisting city councils in reducing logistics costs by automatically planning standardized trips with only the input data required.However, the model's applicability is limited to small instances.Future research could consider employing a Simulated Annealing (SA) metaheuristic to solve larger instances, enabling the planning of future expansions of the selective waste collection system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".