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Record W2118867130 · doi:10.1287/inte.1050.0154

Scheduling Employees in Quebec’s Liquor Stores with Integer Programming

2005· article· en· W2118867130 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueINFORMS Journal on Applied Analytics · 2005
Typearticle
Languageen
FieldDecision Sciences
TopicScheduling and Timetabling Solutions
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsCorporationScheduling (production processes)Integer programmingScheduleOperations researchComputer scienceOperations managementBusinessDatabaseEngineeringOperating systemFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.350
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it