A Proposed Model for Laptop Third-Party Reverse Logistics Provider Selection
Bibliographic record
Abstract
Recognizing the importance of Reverse Logistics (RL), companies have increasingly explored outsourcing their RL activities to Third-Party Reverse Logistics Providers (3PRLPs) for cost saving and enhanced efficiency. These providers specialize in managing processes like recycling, refurbishing, and disassembling, allowing them to mitigate waste and optimize the value of returned items. The selection of a suitable 3PRLP aligned with business goals is critical, given variations in service levels. Therefore, evaluating 3PRLPs plays a pivotal role in establishing and maintaining successful partnerships. This study focuses on the Canadian laptop industry, where used devices are collected, disassembled, and processed by 3PRLPs. In this study, criteria for evaluating 3PRLPs are categorized into three groups: beneficial, non-beneficial, and target-based criteria. The Best Worst Method (BWM) is employed to weigh criteria, and a normalized decision matrix is generated using the target-based method. Then, a modified WASPAS method (target-based WASPAS) is used to evaluate and select the best 3PRLP. The study's findings, obtained through analyses, are discussed in the conclusions section.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".