A periodic repair algorithm for dynamic scheduling in home health care using agent-based model
Bibliographic record
Abstract
This paper presents a periodic repair algorithm for dynamic home visit scheduling with the objective of reducing the service cost in home healthcare. In our setting, the health care agency needs to assign practitioners to cover all home visit requests and, at the same time, respect practitioner's availability, eligibility and patient's visit time constraints. We consider a dynamic scheduling problem in which dynamic events occur along with the execution of existing schedules. The occurrence of dynamic events, such as newly added requests, visit cancellations or availability changes, will render the existing schedule infeasible. We propose a repair-based rescheduling algorithm which periodically revises the existing schedule to accommodate the collection of dynamic events during a certain time period. An agent-based simulation model is developed using AnyLogic to validate the efficiency of the proposed algorithm. Simulation results show that the service cost of the solutions generated by the proposed algorithm is on average 14% lower than that of the solutions generated by first-come-first-serve policy which is commonly used in home health care scheduling practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".