Supplier selection in a multi-item/multi-supplier environment
Bibliographic record
Abstract
Suppliers play a pivotal role in success of any organization. Supplier Selection is a complicated problem due to the vagueness of data and also its multi-criteria nature and the real world still observes a noticeable gap between its theory and practice. The aim of this paper is to present a fuzzy decision-making approach to address this problem in a way that facilitates the process of decision making while not deteriorating its comprehensiveness. The main contributions of the paper are twofold: First, a model is developed to consider multiple suppliers and multiple items. Moreover, a Piecewise Linear Membership Function (PLMF) is proposed for a specific criterion and is shown how it leads to better solutions. The model functions well in cases where decision maker is sensitive about a specific criterion, in other words, when there are some unequal weights for objectives of the problem. Although asymmetric methods proposed by Zimmerman are a way to tackle the above-mentioned situations; it is demonstrated that how the proposed model brings about both efficiency and simplicity for decision maker which is originated from the utilization of PLMF.
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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 | 0.004 | 0.004 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".