Resilience and sustainable supply chain network design by considering renewable energy
Bibliographic record
Abstract
Summary Nowadays, using renewable energy (RE) is faster growing by each country. The managerial and designer of supply chain network design (SCND) have to plan to apply RE in pillars of supply chain (SC). This research indicates resilience and sustainable SCND by considering RE (RSSCNDRE) for the first time. A two‐stage new robust stochastic optimization is embedded for RSSCNDRE. The first stage locates facility location and RE and the second stage defines flow quantity between SC components. We solve the model by GAMS‐CPLEX solver to locate components of SC and RE. Effects of changing conservative coefficient and demand are investigated and by increasing 20% for conservative coefficient, the cost function increase by 0.5%. Also, when demand is high, activating RE is economically feasible and we cannot buy and supply energy by the government power network and have to supply energy by RE. After activating RE, by increasing 20% for demand, the cost function increases by 6%. We contribute fix‐and‐optimize strategy to define the upper bound for a large‐scale problem. The proposed upper bound for the main model is less than 10% and appropriate for estimating the cost of large‐scale problems. This research suggested to equip SC by RE that SC becomes resilient against demand fluctuation and sustainable energy resource compatible with sustainable development goal (SGD7).
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".