Sustainable supply chain design: A case of collaborative wholesale distribution
Bibliographic record
Abstract
We assess the cooperative network design's efficiency outcomes in Morocco's food wholesale distribution. Before joining the collaborative coalition, we provide decision-makers with an initial preference instrument to assess the environmental and financial effects of cooperative freight distribution. We assess the practicality of incorporating decisions from the Facility Location Problem (FLP) and Vehicle Routing Problem (VRP) into partnerships for sustainable freight transportation. The coalition utilizes a 3PL provider's fleet of cars, therefore the Vehicles are exempt from having to go back to the consolidation depot; as a result, the fundamental issue becomes an open location routing problem (OLRP). Although there have been several studies on open location-routing problems, their application to horizontal shipper collaboration is new. For diverse collaboration scenarios, our computational approach is founded on two-echelon OLRP under a multiple objective and periods’ framework. Every shipper involved in the partnership needs to receive gains. Therefore, evaluating the benefits to each individual shipper is essential for an effective and durable collaboration. This study addresses the issue of profit allocation to determine the collective and individual shipper's savings. By considering not just economic variables but also environmental factors, the Open LRP may help firms plan and optimize their collaborative supply chains in a more sustainable manner.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".