K-means clustering for optimization of spare parts delivery
Bibliographic record
Abstract
Transhipment is an important logistics strategy that helps to improve supply chain efficiency and reduce transportation costs. It enables cargo to be transported to multiple destinations using different modes of transportation, such as ships, trains, trucks, and planes. This can help to reduce the overall transportation time and cost, as well as improve inventory management and distribution. In addition to its use in logistics and transportation, transhipment can also be used in other industries such as manufacturing, where it can be used to transfer raw materials or finished products between different facilities or production lines. This research paper examines the role of transhipment in improving the efficiency of spare part delivery systems to the PMPML depots from central workshop Swargate. PMPML has 12 depots in total (including central workshop). In many industries, the supply chain for spare parts is complex, with multiple suppliers, warehouses, and service centres involved. Transhipment, or the transfer of inventory between locations, can help to reduce lead times and improve inventory availability. In this paper, we analyze the impact of transhipment on key performance metrics such as order fulfilment, inventory turnover, and transportation costs. We also discuss the challenges associated with implementing transhipment in spare part delivery systems, including coordination between different parties, data sharing, and system integration.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".