Combined Nearest Greedy Algorithm With Randomized Iterated Greedy Algorithm to Solve Waste Collection Problem
Bibliographic record
Abstract
The waste collection considers as one of the common transportation problems in the operation research and management area and also it is a significant activity in each city. In this kind of the paper, we study a version of a real-life waste collection and try to find an efficient way to reduce the costs such as the cost of the operation such as fuel and maintenances, the cost of the environment such as noise and traffic congestions, the cost of the investment such as vehicles fleet. The waste collection problem can be formulated as a well-known Vehicle Routing Problem (VRP). The basic idea is to attempt to develop a daily truck routing which will improve the efficiency of the vehicle distribution in Riyadh. The solution will be done in a good way that it can serve all the customers, while in the meantime, it will attempt to improve the total cost. The main contribution of this paper is to improve the solution of total costs for the waste collection while using the existing resources through the combination of the Nearest Greedy algorithm with both Iterated Greedy (IG) and Randomized Iterated Greedy (RIG). We execute our proposed method with real data that collect waste from more than 100 customers in Riyadh city. In terms of the experiments, the results received by those methods are successfully implemented and improved the overall waste collection in Riyadh. In conclusion, these algorithms able to reduce the total costs to this kind of case study with the same number of vehicles.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".