Multi-Objective Optimization for Sustainable Closed-Loop Supply Chain\n Network Under Demand Uncertainty: A Genetic Algorithm
Bibliographic record
Abstract
Supply chain management has been concentrated on productive ways to manage\nflows through a sophisticated vendor, manufacturer, and consumer networks for\ndecades. Recently, energy and material rates have been greatly consumed to\nimprove the sector, making sustainable development the core problem for\nadvanced and developing countries. A new approach of supply chain management is\nproposed to maintain the economy along with the environment issue for the\ndesign of supply chain as well as the highest reliability in the planning\nhorizon to fulfill customers demand as much as possible. This paper aims to\noptimize a new sustainable closed-loop supply chain network to maintain the\nfinancial along with the environmental factor to minimize the negative effect\non the environment and maximize the average total number of products dispatched\nto customers to enhance reliability. The situation has been considered under\ndemand uncertainty with warehouse reliability. This approach has been suggested\nthe multi-objective mathematical model minimizing the total costs and total CO2\nemissions and maximize the reliability in handling for establishing the\nclosed-loop supply chain. Two optimization methods are used namely\nMulti-Objective Genetic Algorithm Optimization Method and Weighted Sum Method.\nTwo results have shown the optimality of this approach. This paper also showed\nthe optimal point using Pareto front for clear identification of optima. The\nresults are approved to verify the efficiency of the model and the methods to\nmaintain the financial, environmental, and reliability issues.\n
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".