Application of Multiagent Simulation for Maintenance Workflow Management and Resource Allocation in Hospital Buildings
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.
Bibliographic record
Abstract
Facility managers of hospitals face complex maintenance decisions as they deal with a multitude of maintenance requests in an environment of limited resources and segmented information. Responding to a growing demand for maintenance, on one hand, and lack of proper maintenance management systems, on the other, has led to delays in repair and maintenance of the building components and systems in hospitals. Such delays could cause significant distress to patients and health care personnel. This paper introduces a new method for facility managers to address these challenges. The multimethod simulation approach is developed to integrate segmented information at different levels of maintenance management, with the aim of minimizing maintenance delays in hospital buildings. The developed simulation model consists of two components: a status tracking system (STS) and a resource allocation system (RAS). A discrete event simulation (DES) is used to simulate the maintenance process flow while a multi-agent system (MAS) is used to simulate the process of allocating resources for maintenance activities in hospital buildings. The STS simulation is a DES process that registers, arranges, and distributes maintenance tasks (orders) to the appropriate resources. For the RAS component, a multi-agent resource allocation system (MARAS) is developed to simulate different resource allocation scenarios, accounting for interactions among various agents (decision-makers) in the maintenance process. A case example is presented to demonstrate the essential features of the developed method. The simulation results show that the implementation of MARAS significantly reduces maintenance delays in the case study.
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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 | 0.000 | 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 | 0.000 | 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 it