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 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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; both teacher heads agree on what is shown here.
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".