Designing a robust supply chain management based on distributers’ efficiency measurement
Bibliographic record
Abstract
An appropriate supply chain design helps survival in competitive markets.Achieving maximum efficiency may also help decision makers have a better selection for the supply chain network.The purpose of this paper is to design an efficient supply chain model in terms of the distribution channels under uncertain conditions.The proposed study produces multi products using different materials by considering four layers of multiple suppliers, producers, storages and customers.There are two objectives of maximizing efficiency of distributers and minimizing total cost of supply chain management.The proposed model locates producers as well as suppliers and determines the amount of orders from different suppliers.In order to measure the relative efficiency, the study uses the method developed by Klimberg and Ratick (2008) [Klimberg, R. K., & Ratick, S. J. (2008).Modeling data envelopment analysis (DEA) efficient location/allocation decisions.Computers & Operations Research, 35(2), 457-474.].In addition, to handle the uncertainty, the study uses the robust optimization technique developed by Molvey and Ruszczyński (1995) [Mulvey, J. M., & Ruszczyński, A. (1995).A new scenario decomposition method for large-scale stochastic optimization.Operations research, 43(3), 477-490.].The preliminary results indicate that the proposed model is capable of providing efficient solutions under various uncertain conditions.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".