Sustainable supply chain planning and optimization trade-offs between cost, GHG emissions and service level
Bibliographic record
Abstract
Sustainable supply chain planning plays an important role to achieve sustainable operations and logistics. Sustainable supply chain performance is based on economic, environmental and social impacts. In this paper, we present a multiobjective decision making framework for sustainable supply chain optimization. We consider a supply chain network consisting of production plants, distribution centers and retailers (customers). A multi-product and multi-period planning model is proposed. Sustainability is evaluated based on three performances: cost, GHG emissions, and service level. We use this model in test case of Frozen Food industry. Preliminary experimentation demonstrates that the three objectives are conflicting. However, just in time distribution might increase total cost but reduce GHG emissions due to the best control of inventories at distribution centers and retailers. Indeed, the energy consumption caused by storing pallet of frozen food decrease. On the other hand, total cost optimization lead to service level decrease with more efficient transportation (Full track Load Delivery) and reduce GHG emission of transportation activities. Finally, the decision making model helps to identify the trade-off between the three conflicting objectives, and take the best decisions to achieve sustainability objectives of the supply chain.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".