Multi-Objective Optimization for Sustainable Closed-Loop Supply Chain Network Under Demand Uncertainty: A Genetic Algorithm
Bibliographic record
Abstract
Productivity can be increased through sophisticated supply chain management including vendor, manufacturer, and the final consumer. Recently, energy and material consumption have been greatly increased. As a result, the problem has increased for sustainable development for developed and developing countries. Hence, in this paper a novel approach of supply chain management is proposed to maintain the economic and the environment issues for the design of supply chain as well to ensure the customer demand. The objectives of this paper is to optimize a new sustainable closed-loop supply chain network to maintain the economic along with the environmental factor to minimize the negative effect on the environment and maximize the average total number of products dispatched to customers to enhance satisfaction. The demand uncertainty and warehouse reliability has been considered in this model. This multi-objective mathematical model minimizes the total costs and total CO2 emissions and maximize the customer reliability through establishing a closed-loop supply chain network. Multi-Objective Genetic Algorithm Optimization Method and Weighted Sum Method are used to solve the proposed problem. The results indicate the optimality of the proposed problem through Pareto front. The obtained result validates the model to maintain economic, environmental, and reliability aspects.
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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 | 0.001 | 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".