A fuzzy rule-based approach to prioritize third-party reverse logistics based on sustainable development pillars
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
An efficient reverse logistics structure plays an important role in improving market competitiveness. The complexity of reverse logistics operations, customer service improvement, and costs elimination highlight the necessity of reverse operations outsourcing to the third-party reverse logistics providers (3PRLPs). Investigating and selecting an appropriate 3PRLP is recognized as a significant issue by manufacturers. This problem is affected by uncertainty, basically due to the vagueness intrinsic to the assessment of qualitative factors. This paper aims to propose a structured approach to prioritizing 3PRLPs based on sustainability criteria under fuzzy environment which accommodate the uncertainty associated with the vagueness of qualitative criteria. The proposed approach is composed of two main steps in which the first step employed the fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) to select the effective criteria and the second step used Mamdani Fuzzy Inference System (FIS) model to cope with the vagueness that exists in the 3PRLPs evaluation process. If-then scenarios are employed to design rules of a FIS model which are devised by experts. The Experts’ knowledge about the problem is incorporated into the FIS system. This is a significant benefit of the proposed approach, in comparison with approaches which incorporate fuzzy set theory with multi-criteria decision-making models. An industrial case study is conducted to highlight the real-life applicability of the proposed approach. In addition, a sensitivity analysis is performed to confirm the robustness.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it