Scotsburn Dairy Group Uses a Hierarchical Production Scheduling and Inventory Management System to Control Its Ice Cream Production
Bibliographic record
Abstract
In this paper, we discuss a hierarchical production planning approach to schedule ice cream production, a continuous batch production process with sequence-dependent setup times and highly seasonal demand. We use mixed-integer linear programming models to optimize production plans for long-, medium-, and short-term planning. The long-term model, the monthly model, is used to plan aggregate production and inventory levels for the year to meet demand each month at the lowest cost possible. The medium-term model, the weekly model, is used to disaggregate the long-term plan to minimize weekly setup and holding costs over a 13-week period. The short-term model, the daily model, is a detailed scheduling model that determines an optimal daily production sequence for the products and run lengths determined by the second model, while meeting the labor schedule defined by the first model for the upcoming production week. When used together, the three decision models produce feasible results at each stage, and short-term operations reflect the goals of the long-term plan. Synchronizing the model breakdown with Scotsburn’s management hierarchy provides support at each decision-making level. The hierarchical plan reduces costs, improves production efficiency, and creates better linkages between the decisions of each management level.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".