Bibliographic record
Abstract
This paper describes the appointment scheduling game (ASG), an easy to use teaching tool that reveals the challenges in managing advance patient scheduling systems, and also provides an introduction to simulation and decision analysis. In addition to describing the game, the paper provides recommendations on how to play it, student questions and suggested answers, and a Markov decision process (MDP) formulation. The ASG simulates a system in which daily patient appointment requests, which are characterized by their urgency level, arrive randomly. Daily service capacity is limited. Students playing the game assume the role of a scheduling clerk who must assign appointment dates to these requests without knowing future demand. They are left to discover the need for performance metrics, data collection, and strategy formulation. An attractive feature of the game is that it requires only a printed one-month calendar, multicolored poker chips, and a standard six-sided die. Although the game is primarily aimed at undergraduate and graduate operations students, it also can be used to introduce a range of MDP concepts to advanced operations research students. The game has been used successfully in several courses at the University of British Columbia including “Managing Health Care System Operations” (MBA), “Managing Patient Flow” (executive MBA in healthcare) and “Logistics and Operations Management” (undergraduate). It has also been used by colleagues at the University of Ottawa, McGill University, and the University of Michigan.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".