An integrated vendor–buyer replenishment policy for deteriorating items with fuzzy environment and resource constraint
Why this work is in the frame
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Bibliographic record
Abstract
Different from previous researches, our study considers a perishable item with collaborative vendor–buyer ordering policy and finite replenishment rate. Furthermore, due to the importance of inventory and capital investment in today’s fuzzy marketing environments, researches in fuzzy collaborative inventory models have become very popular research in recent decades. Therefore, in our integrated model with deteriorating inventory replenishment policy, we construct the crisp/fuzzy models with inventory investment constraints with fuzzy environments. Two different fuzzy decision-making methods are used to formulate the models. Convex fuzzy programming (CFP) method is used to maximize the weighted sum for each achievement level of the joint cost and constraints. An inverse weighted fuzzy non-linear programming (IWFNLP) is then proposed to satisfy the decision-maker’s desirable achievement level of service. A heuristic algorithm with mixed-integer hybrid differential evolution (MIHDE) is developed to solve the crisp/fuzzy models. Numerical examples and sensitivity analysis are developed to investigate the effectiveness of the proposed method in the fuzzy environment. From the numerical analysis, it can be seen that the IWFNLP method is a more efficient decision-making tool than the CFP method.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| 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