Scheduling Employees in Quebec’s Liquor Stores with Integer Programming
Bibliographic record
Abstract
The SAQ (in French, Société des alcools du Québec) is a public corporation of the Province of Quebec responsible for distributing and selling alcohol-based products in its territory through a large network of more than 400 stores and warehouses. Every week, the SAQ has to schedule more than 3,000 employees. Until 2002, it handled this process manually, incurring estimated expenses of $1,300,000 (CAN). I developed a solution engine that interacts with a Web-based database system developed in house to produce the desired schedules. This solution engine implements an integer-programming (IP) model using ILOG Concert Technology and solves the IP formulation with ILOG CPLEX. The project has contributed to increasing the efficiency of the organization by reducing the costs of producing the schedules and by improving the SAQ’s management of human resources. Overall, the SAQ estimates that automated scheduling has saved over $1,000,000 (CAN) annually.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".