Application of Multiagent Simulation for Maintenance Workflow Management and Resource Allocation in Hospital Buildings
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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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".