Optimal Routing in Supply Chain Network Design
Bibliographic record
Abstract
This study aims to design a transportation network in a fresh banana supply chain to minimize the distance, route, and vehicles.The design of the network is based on graph theory, which is the Cheapest Insertion Heuristic Algorithm for Traveling Salesman Program, and is expected to guarantee fresh banana products in a supply chain starting from a supplier of seed, compound fertilizer, PTPN VIII, distribution center (ripening stage), retailers, and consumers.Design of the transportation system in the supply chain of fresh bananas.There are two distribution centers and 26 retail locations in the two cities that must guarantee the availability of fresh bananas to meet the demands of the community in West Java Province.The distribution of fresh bananas in a transport system uses the cheapest insertion heuristic algorithm.Optimization of a fresh banana transportation system will streamline supply chain activities in PTPN VIII, thus ensuring the fulfillment of the demand and availability of fresh banana products in West Java Province.This research has implications for the performance of PTPN VIII in improving the optimization of fresh banana supply chain systems.This study will explicitly describe the distance, routes and optimum vehicle types in order to minimize transportation costs, resulting in a transportation cost reduction of 25.24%.
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 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 | 0.002 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".