Strategic planning problem represented by a three-echelon logistics network-modeling and solving
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it