Green Drug Supply Chain Investigation by Time-Market Balance and Risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The quality of pharmaceutical products plays a crucial role in healthcare systems such as hospitals for better patient services. Drug Supply Chain Management requires approaches to uncertainty and risk consideration. This study is a comprehensive multi-objective mathematical model considering the uncertainties and potential reserves in supply and medicine. The proposed model includes three general objective functions that minimize total production costs, including the costs of transportation, maintenance, breakdown, collection, and disposal of waste. The model also maximizes the quality of potential storage. The results show the proposed method has a high quality to solve the model and leads to the optimization of the results to provide the drug supply chain for the proposed example. We have identified three important risks and uncertainties in addressing drug supply planning: the indefinite duration of the licensing process, the risk of a forced brand change, and indefinite repayment levels that lead to varied demand diversification. The results of comparison with other multi-objective optimization methods in existing articles also show better performance of the proposed model. A significant cost reduction results from implementing our model instead of using the over-storage role to estimate the volume of active drug elements, as seen in today’s industry.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it