Intégration des objectifs du développement durable dans la gestion stratégique et tactique de la chaîne logistique
Bibliographic record
Abstract
We address the problem of supply chain management in the context of CSR (Corporate Social Responsibility). We propose an integrated approach allowing the operationalization of the economic, environmental and social performances at the strategic, tactical and operational decision levels of the supply chain. In particular, we apply our approach to the strategic and tactical levels, for the problem of sustainable insourcing/outsourcing of the activities of the value chain on the one hand (strategic decision), and for the problem of the strategic-tactical planning of a sustainable supply chain on the other hand. In the former case, we combine value analysis with performance measurement using AHP method (Analytical Hierarchy Process), an aggregative multi-criteria technique. In the latter, we develop a multi-objective mathematical program that we apply to a realistic case inspired by the Canadian lumber industry. After solving the problem, we obtain a multitude of compromise solutions (Pareto optimums) presenting different performance levels following the economic, environmental and social dimensions, allowing the decision maker to choose the solution that reflects best his/her CSR strategy. This application illustrates the proposed method and allows us to assess the practical value of our approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".