Fuzzy multi-objective optimization with α-cut analysis for supply chain master planning problem
Why this work is in the frame
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Bibliographic record
Abstract
This study considers a supply chain master planning problem in an uncertain environment where operating costs, customer demand, production capacity, manufacturer's acceptable defective rate, and manufacturer's acceptable service level are uncertain. Our supply chain consists of one manufacturer, multiple suppliers, and multiple distribution centers. While one objective is to minimize the total costs of logistics that consists of purchasing cost, production cost, and distribution cost, the other objective is to maximize total value of purchasing. These objectives are in conflict with each other. In this paper, the fuzzy multi-objective linear model is applied with -Cut analysis to achieve the optimal supply chain master planning in an uncertain environment by balancing these two conflicting objectives. The -Cut analysis is introduced to ensure decision-makers that the outcome satisfies their preferences based on a specified minimum allowed satisfaction value ( ).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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