An integrated logistics model for environmental conscious supply Chain network design
Bibliographic record
Abstract
Operations Research has addressed a variety of environmental problems outside the traditional supply chain management area such as remanufacturing, reverse logistics, and waste management. Supply chain sustainability, which includes designing green supply chains, will gain much more attention in the future. Indeed, most companies are still in the early stage of considering a green initiative. Traditionally, optimization models for supply chain network design looked to different strategic network alternatives, and analyze the trade-offs between logistics costs and service requirements. Today, with the strong emphasis in reducing greenhouse gas footprint, the integration of such consideration into the supply chain network design phase will provide to companies much more visibility on how to manage efficient, effective, and green supply chains. In this paper, a mathematical programming model for environmental conscious supply chain network design is introduced with the explicit inclusion of carbon emission cost. By considering the greenhouse gases emissions cost together with traditional logistics costs, the problem is formulated as a single objective optimization program. The methodology uses mixed integer linear programming modeling technique to deal with different strategic decisions, including supplier and subcontractor selection, product allocation, capacity utilization, and assignment of transportation links required to satisfy market demand. This new formulation provides decision makers with a quantitative decision support system to understand the tradeoffs between the total logistics cost and the carbon footprint reduction.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".