Third-Party Reverse Logistics Selection: A Literature Review
Bibliographic record
Abstract
Background: This literature review delves into the concept of ‘Third-party Reverse Logistics selection’, focusing on its process and functionality using deterministic and uncertain decision-making models. In an increasingly globalized world, Reverse Logistics (RL) plays a vital role in optimizing supply chain management, reducing waste, and achieving sustainability objectives. Deterministic decision-making models employ predefined criteria and variables, utilizing mathematical algorithms to assess factors such as cost, reliability, and capacity across various geographical regions. Uncertain decision-making models, on the other hand, incorporate the unpredictability of real-world scenarios by considering the uncertainties and consequences of decision making and choices based on incomplete information, ambiguity, unreliability, and the option for multiple probable outcomes. Methods: Through an examination of 41 peer-reviewed journal publications between the years 2020 and 2023, this review paper explores these concepts and problem domains within three categories: Literature Reviews (LR), Deterministic Decision-Making (DDM) models, and Uncertain Decision-Making (UDM) models. Results: In this paper, observations and future research directions are discussed. Conclusions: This paper provides a comprehensive review of third-party reverse logistics selection papers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".