Dynamic reverse supply chain network design under uncertainty: mathematical modeling and solution algorithm
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
Abstract Motivated by the recovery of modular‐structured products, this study addresses the flexible design of a reverse supply chain (RSC) over a planning horizon while incorporating the dynamic uncertain behavior of product returns. The stochastic parameter is modeled as a scenario tree and therefore the concerned problem is formulated as a multistage mixed‐integer stochastic program. To alleviate the computational complexity of the proposed model, it is decomposed into smaller scenario cluster submodels associated with a number of subtrees that share a certain number of predecessor nodes in the original scenario tree. The submodels are coordinated into an implementable solution via a Lagrangian‐progressive hedging‐based method that employs a viable Benders decomposition based algorithm for solving each scenario cluster submodel. Based on a realistic scale case, computational results indicate the superiority of the proposed flexible dynamic RSC design model compared to the existing models. Results also demonstrate the efficiency of the proposed solution approach.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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