Strategic planning problem represented by a three-echelon logistics network-modeling and solving
Bibliographic record
Abstract
This article aims to elaborate a strategic plan allowing to decision makers to take right decisions (Selecting suppliers, Selecting plants that can produce a specific product,...) in the right moment in order to minimize the generated costs. Our work consists, then to optimize a multi-scales and multi-periods location-distribution problem. The problem belongs to the FLNP family with a complexity of order of NP-difficult. The objective of our problem MIP is to maximize the incomes of a production company via the minimization of costs: the cost of supplying, the cost of producing and the cost of transportation. Several aspects would be treated in this subject: the horizon of planning-multi-periods and the structure of network (multi-echelons). Based on the limits of exact methods, we have proposed to resolve this problem on the basis of a heuristic method, the choice which seems to be the most adequate for our problem is LNS (Large Neighborhood Search). It is in this perspective that we have reformulated our model [12] in order to be represented under the form of a logistic network based on paths before the application of LNS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".