Abstract:Emergency departments (ED) face challenges in patient care and resource management. We propose to explore optimization strategies in a realistic and flexible model and develop a hybrid Discrete Event Simulation (DES) and Agent-Based Model (ABM) simulating highly configurable ED environments. We specifically focus on the validation of the modeling approach. We derive configurations for ED sizes, patient load, and staffing from real-world studies. We then validate the model expressivity by matching its key performance indicators and metrics with their values known from literature. We proceed by implementing scientifically established and practice-proven resource optimization strategies. Comparing the documented real-world outcomes with our model's results demonstrates that the DES-ABM based simulation can effectively replicate real-world ER dynamics under interventions. We lastly integrate a Proof-of-Concept multi-agent system (MAS) that can autonomously explore resource allocation strategies within the simulated ER environment based on a temporal ledger of ED event records. This modular DES-ABM-MAS framework offers a powerful tool to explore resource optimization strategies in emergency departments.
| Subjects: | Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) |
| MSC classes: | 37 |
| Cite as: | arXiv:2605.13345 [cs.AI] |
| (or arXiv:2605.13345v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2605.13345 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Markus Wenzel [view email]
[v1]
Wed, 13 May 2026 11:04:23 UTC (2,922 KB)