Modeling and Analysis of Multicommodity Network Flows Via Goal Programming
Bibliographic record
Abstract
In this work goal programming is used to solve a minimum cost multicommodity network flow problem with multiple objectives. The network consists of; linear objective function.linear cost arcs, fixed arc and node capacities, and specific origin-destination pairs for each commodity. This suggests a classic linear program. When properly modeled. Lagrangian relaxation. Daiitzig-Wolfe decomposition, and network flow techniques may be employed lo exploit the pure network structure. Lagrangian relaxation captures the essence of Ihe pure network flow problem as a master problem and sub-problems. The relaxation may be optimized directly, or be decomposed into subproblems, one tor each commodity with eaeh subproblem a minimum cost single commodity network flow problem. Postoptimalily analyses, viasensitivity analysis and parametric analysis, provide a variety of options under which the robustness of the optimal solution may be investigated. This mix of modeling options and analyses provides a powerful approach for producing insight into the modeling of a multicommodity network flow problem with multiple objeetives.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".