A fuzzy rule-based approach to prioritize third-party reverse logistics based on sustainable development pillars
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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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".