An integrated reliable five-level closed-loop supply chain with multi-stage products under quality control and green policies: generalised outer approximation with exact penalty
Bibliographic record
Abstract
In this paper, we design and optimise an integrated five-level Supply Chain (SC), which contains a supplier, a producer, a wholesaler, multiple retailers, and a collector. Accordingly, a Closed-loop Supply Chain (CLSC) with multi-stage products is designed with respect to the green production principles and Quality Control (QC) policy under back-logged and lost sale types of the shortage. Levels cooperate with each other to make an Integrated Supply Chain (ISC) so that the total cost function is minimised and the total reliability function is maximised, simultaneously. The model is constrained by real stochastic constraints. The total inventory cost includes the ordering costs, holding costs, shortage costs, setup costs, production costs, screening costs, reworking costs, disposal costs, tax cost of GHG emissions, collection costs, and disassembling costs. The final objective is to optimise the number and volume of the stockpiles of the products. The integrated CLSC model is a hyper-scale Mixed Integer Nonlinear Programming (MINLP) model. In this regards, a Generalised Outer Approximation with Exact Penalty (GOA/EP) is presented to optimise the MINLP model of research based on decomposition principles, Outer Approximation (OA), and relaxation techniques. Numerical analyses revealed the excellent performance of the presented method for solving the hyper-scale MINLPs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".