1. The article I found online was titled, “A GENERALIZED SYMBIOTIC SIMULATION MODEL OF AN EMERGENCY DEPARTMENT FOR REAL-TIME OPERATIONAL DECISION-MAKING” and was written by Alexander R. Heib, Christine S. M. Currie, Bhakti Stephan Onggo, and Honora K. Smith (University of Southampton) and James Kerr (Hampshire Hospitals NHS Foundation Trust). This article was found from the provided link, Winter Simulation Conference ArchiveLinks to an external site., and the link to the article is 087.pdf (informs-sim.org)Links to an external site..
The simulation application was designed to improve short term decision making in emergency rooms in the United Kingdom in Europe. The overall aim being to more efficiently admit patients in the ER while saving time for both the workers and the patients. According to Heib et al, “By examining the outputs of the model, we were able to verify visually that patients were being placed into the appropriate queues and exiting the system. As the simulation-optimization routine will need to have a small execution time, ideally under 20 minutes, another aspect of the simulation validation was ensuring that the model ran fast enough. One simulation run took on average 0.05 seconds to complete.” (Heib et al, 2023) The benefits on the simulation have shown that they are reducing inefficiencies in the emergency room.
I chose this article because although this article specifically addresses the emergency rooms in Europe and the United Kingdom, I have waited a long time in an emergency room in the United States, so I think this is a very good simulation idea. It makes a lot of sense that a simulation could be built for majority of patients/cases to provider a quicker experience for all.
References:
A Generalized Symbiotic Simulation Model of an Emergency Department for Real-Time Operational Decision-Making
2.
Title Autonomic Orchestration of In-Situ and In-Transit Data Analytics for Simulation Studies
Author Xiaorui Du (Technical University of Munich); Adriano Pimpini (Sapienza, University of Rome); Andrea Piccione (Huawei Munich Research Center); Zhuoxiao Meng and Anibal Siguenza-Torres (Technical University of Munich); Stefano Bortoli (Huawei Munich Research Center); Alois Knoll (Technical University of Munich); and Alessandro Pellegrini (University of Rome Tor Vergata)
Source WSC
Link https://informs-sim.org/wsc23papers/065.pdfLinks to an external site.
This article discusses how modern distributed simulations produce large amounts of data. Performing analyses on the end of simulations can cause bottlenecks. Processing data produced by simulations can also impair computational performance and overload the network. The authors present a methodology and architecture for constructing an autonomic control system to determine best runtimes for data processing. Allowing a tradeoff between load on the simulation’s path and data system. This allows for improved processing time of data, reducing end-to-end completion time for evaluating queries.
I selected this article because it was based on data analytics. In an environment where businesses are transitioning to AI, machine learning and data analytics, this will lead to processing more and more data. Most data requires extensive cleaning and filtering before it can be processed leading to many delays in processing times.
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