Development of a Catch Allocation Tool Design for Production Planning at
Bibliographic record
Abstract
JS McMillan Fisheries Ltd. (JSM) is a Vancouver-based commercial fishing, production and distribution company. As the operations of JSM evolved, the process of allocating a commercial salmon catch to a set of final products has become complex and time-consuming. We developed a linear programming based decision support tool to assist JSM management with this allocation decision. The decision support tool yields a production plan that maximizes the profit potential of the catch and allows management to carry out “what if ” analyses. Moreover, this paper explores implementation issues such as modeling fish quality deterioration, measuring the effect of byproduct and addressing catch-size uncertainty. Key words: Fish processing planning, decision support tools, production planning, linear and stochastic programming. RÉSUMÉ JS McMillan (JSM) est une compagnie de pêche professionnelle, de production et de distribution basée en Vancouver. Avec l’évolution des opérations de JSM, le processus de la prise de décision pour assigner un crochet de saumon parmi un ensemble de produits finals est devenu trop complexe et long. Nous avons développé un outil de support de décision basé sur la programmation linéaire pour aider la gestion de JSM avec cette décision d’attribution. L'outil de support de décision rapporte un plan de production qui maximise le potentiel de profit du crochet et permet la gestion d’effectuer l’analyse de scénario. D’ailleurs, cet article explore des issues d'exécution telles que modeler la détérioration de qualité de poissons, mesurer l'effet du sousproduit et adresser l'incertitude de taille de crochet. Mots-clés: La planification de traiter la pêche, la programmation d'outil de support de décision, la planification de la production, la programmation linéaire et stochastique. 1.
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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 | 0.003 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".