A Binary, Non-convex, Variable-capacitated Supply Chain Model
Bibliographic record
Abstract
This paper is concerned with three-echelon supply chain design where each supplier provides a unique set of goods from known, possibly multiple, locations and where each outlet has a fixed, known demand so that it exhibits the features of the supply chain of an existing company that operates across Canada and in the United States of America (35 suppliers, 83 potential DC locations and 2, 976 outlets). A mathematical model is presented whose so-lution determines the location and capacity level of Distribution Centers (DCs) and assigns outlets to the selected DCs. The model is unique in that it allows true variability in the choice of capacity level and so avoids the need to determine, a priori, a set of potential capacity levels. The design objective is to minimize fixed and variable costs for operations and transportation that ac-count for decreasing marginal costs and economies of scale. This makes the model a binary, non-convex optimization problem. A piecewise linear approxi-mation to the concave cost functions that captures the concept of “technology break-points” results in a model for which LINGO can quickly determine high quality solutions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".