An integrated reliable four-level supply chain with multi-stage products under shortage and stochastic constraints
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we design and optimise an integrated four-level Supply Chain (SC), which contains a supplier, a producer, a wholesaler and multiple retailers. The levels cooperate to make an Integrated SC (ISC) so that the inventory cost is minimised and the reliability is maximised, simultaneously. The model is constrained by real stochastic constraints on total space, number of orders, procurement cost, shortage cost, setup cost and production capacity. An Lp-Metric function converts the reliability function and cost function into a single-objective function to optimise the number of stockpiles and period lengths. The designed ISC is a large-scale Nonlinear Programming (NLP) and hard to solve by generic methods. Accordingly, two algorithms, entitled ‘Sequential Quadratic Programming (SQP)’ and ‘Interior Point (IP)’ with super-linear convergence rates are applied for finding the optimum solution. The performance of proposed algorithms is compared based on optimality criteria. Findings showed that the obtained solutions by SQP algorithm have better performance than IP algorithm in terms of optimality error and solution quality. However, the number of taken iterations by IP is less than SQP algorithm. Finally, the result of sensitivity analyses confirmed the excellent performance of the presented methods for solving the large-scale NLP models.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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