A Simulation of Patient Queuing System on MRI System at Tertiary Referral Hospital in Indonesia

Radiology department at a tertiary referral hospital faces service operation challenges such as huge and various patient arrival, which can increase the probability of patient queuing. During COVID-19 pandemic, it is mandatory to apply social distancing protocol in the radiology department. A strategy to prevent accumulation of patients at one spot would be required. The aim of this study is to identify an alternative solution which can reduce the patient's waiting time in MRI services. Discrete event simulation (DES) is used for this study by constructing several improvement scenarios with Arenao simulation software. Statistical analysis is used to test the validity of base case scenario model, and to investigate performance of the improvement scenarios. The result of this study shows that the selected scenario is able to reduce 83.6% of patient's length of stay, which lead into a more efficient MRI services in radiology department, be able to serve patients more effectively, and thus increase the patient satisfaction. The result of the simulation can be used by the hospital management to improve the operational performance of the radiology department. © 2021 IEEE.

Emergency Department Management Using Regression Models

COVID-19, a global pandemic has been ravaging the world. The Emergency departments are flooded because of this global pandemic. To provide a good service in the Emergency Departments (ED) in hospitals as a part of smart healthcare, tools that analyze, program, plan or prioritize is required to use the available resources (staff and treatment equipment) in a fine possible way. In this paper, the various queuing methods that are implemented to tackle the patient flow in EDs as well as outpatient departments in the already existing systems are surveyed, and a method is suggested. The previous papers have taken queuing theory into account. Here three different regression techniques namely Linear regression, polynomial regression, and support vector regression are considered for the prediction of the patient flow in Emergency Departments. The hazardous COVID-19 pandemic and its impact on the mounting crisis in the EDs is also discussed. The challenges and suggestive methods are also discussed here. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Mobile Games at the Edge: A Performance Evaluation to Guide Resource Capacity Planning

Mobile games are very popular among young generations, especially during the worldwide Covid-19 pandemic. The pandemic has caused an enormous increase in data transactions and computation over the Internet. Computing for games often consumes a vast amount of computational resources. Nowadays, mobile devices require heavy computing tasks. For this reason, edge computing resources are essentially needed in the game industry for non-latency data transactions. However, edge computing involves many aspects that make its architecture highly complex to evaluate. Pure performance evaluation of such computing systems is necessary for real-world mobile edge computing systems (MEC) in the game industry. This paper proposes a closed queuing network to evaluate the performance of a game execution scenario in MEC. The model permits the evaluation of the following metrics: mean response time, drop rate, and utilization level. The results indicate that the variation in the number of physical machines (PM) and virtual machines (VM) has a similar impact on the system's overall performance. The results also show that dropped messages can be avoided by making small calibrations on the capabilities of the VM/PM resources. Finally, this study seeks to assist the development of game computing systems at MEC without the need for prior expenses with real infrastructures.

DESIGNING AIRCRAFT TRAFFIC FLOWS USING DATA-DRIVEN QUEUING MODELS

This paper proposes a scientific and systematic method for designing future air traffic management systems by integrating data science, theoretical modeling, and simulation evaluation. Also, it presents a part of a case study focusing on the data-driven and theoretical modelings of arriving traffic flow in airports. A stochastic data analysis was conducted using actual radar tracks and flight plans before the impacts of COVID-19, where the queuing model parameters were estimated based on the conducted analysis. The proposed data-driven modeling approaches contribute to the analysis of the bottlenecks in air traffic and to their solutions. Overall, we believe that the outcomes of this study provide insights on future operational strategies and system designs, which can realize more efficient air traffic management systems. © 2021 32nd Congress of the International Council of the Aeronautical Sciences, ICAS 2021. All rights reserved.