SysML for conceptual modeling and simulation for analysis: A case example of a highly granular model of an emergency department

Author(s):  
Ola G. Batarseh ◽  
Eric J. Goldlust ◽  
T. Eugene Day
Author(s):  
Manel Saad Saoud ◽  
Abdelhak Boubetra ◽  
Safa Attia

In the last decades, multi-agent based modeling and simulation systems have become more increasingly used to model the dynamic and the complex healthcare systems which contain many variabilities and uncertainties such as the hospital emergency departments (ED). Modeling and creating virtual societies almost identical and similar to the reality are considered as the strongest advantages of these agents systems. However, during the dynamic development of the artificial societies, a massive volume of data, which generally contains non-express and shrouded information and even knowledge, is involved. Therefore, dealing with this data, to study and to analyze the unclear relationships and the emerging phenomena, is a well-known weakness and bottleneck that the multi-agent systems is suffering from. In conjunction, data mining techniques are the most powerful tools that can help simulation experts to tackle this issue. This paper presents an ongoing research that combines the multi-agent based modeling and simulation systems and data mining techniques to develop a decision support system to improve the operation of the emergency department.


SIMULATION ◽  
2017 ◽  
Vol 94 (6) ◽  
pp. 493-506 ◽  
Author(s):  
Jose J Padilla ◽  
Saikou Y Diallo ◽  
Christopher J Lynch ◽  
Ross Gore

This paper reports on a survey capturing modelers’ perspectives of Modeling and Simulation (M&S). The survey was completed by a total of 283 respondents from the M&S community with 167 fully completed surveys and 151 respondents identified as model builders. Participants include people from government, academia, and industry in varied roles ranging from researchers to business developers. Respondents also represent a diverse educational background ranging from oceanography, social sciences, and engineering. The survey focuses on three dimensions namely: (a) models and simulations, (b) participants, and (c) how participants interact with models/simulations. We provide six observations from the data analysis: there is no dominating paradigm in M&S, the agent-based community is distinct from the discrete-event community, conceptual modeling is the art of M&S, simulation verification is mostly a trial and error activity, validate by all means necessary, and model accreditation is still too uncommon. A key finding from these observations is the identification of an over-reliance on informal methods for conceptualization and verification in M&S. We posit that this over-reliance on informal methods challenges model/simulation validity.


Author(s):  
Manel Saad Saoud ◽  
Abdelhak Boubetra ◽  
Safa Attia

In the last decades, multi-agent based modeling and simulation systems have become more increasingly used to model the dynamic and the complex healthcare systems which contain many variabilities and uncertainties such as the hospital emergency departments (ED). Modeling and creating virtual societies almost identical and similar to the reality are considered as the strongest advantages of these agents systems. However, during the dynamic development of the artificial societies, a massive volume of data, which generally contains non-express and shrouded information and even knowledge, is involved. Therefore, dealing with this data, to study and to analyze the unclear relationships and the emerging phenomena, is a well-known weakness and bottleneck that the multi-agent systems is suffering from. In conjunction, data mining techniques are the most powerful tools that can help simulation experts to tackle this issue. This paper presents an ongoing research that combines the multi-agent based modeling and simulation systems and data mining techniques to develop a decision support system to improve the operation of the emergency department.


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