A Hybrid Genetic-Variable Neighborhood Algorithm for Optimization of Rice Seed Distribution Cost
Bibliographic record
Abstract
Companies engaged in agro-industry, such as rice seed companies, depend on an efficient distribution process because of the characteristics of rice seed products that are easily damaged and do not last long. The distribution and delivery of goods from the production plant to reach consumers must go through several local distributors in several areas (multi-level) such as distributor centers, retailers, and agents spread across several cities in East Java Province. Determining the distribution network will be more complex when a company produces more than one type of product (multiproduct). Based on previous research, the genetic algorithm (GA) has been proven to provide the best solution for various optimization and combinatorial problems. However, the application of classical GA has the drawback that it has not yet reached the optimum global point, so it needs to be hybridized using a variable neighborhood search (VNS) algorithm. VNS was chosen because it can find solutions globally and find solutions locally to cover the shortcomings of GA. Using hybridization of GA-VNS, the cost obtained is 32392960, as evidenced by the cost savings of 323190 compared to the classic GA of 32716150. GA-VNS takes relatively the same time as classic GA.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".