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Modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach

Available hospital evacuation simulation models usually focus on the movement of the evacuees while ignoring the crucial behavioural factors of the evacuees’ which impact the simulation results. For instance, the issue of patient prioritization behaviour during evacuation simulation is often overl...

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Main Authors: Mohammad Abir, Intiaz, Mohd Ibrahim, Azhar, Toha, Siti Fauziah, Mohd Romlay, Muhammad Rabani
Format: Article
Language:English
English
Published: Springer Nature 2024
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/110540/1/NCAAIntiaz.pdf
http://irep.iium.edu.my/110540/7/110540_Modelling%20and%20simulation%20of%20assisted%20hospital_SCOPUS.pdf
http://irep.iium.edu.my/110540/
https://link.springer.com/article/10.1007/s00521-023-09389-w
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spelling my.iium.irep.1105402024-07-29T03:39:27Z http://irep.iium.edu.my/110540/ Modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach Mohammad Abir, Intiaz Mohd Ibrahim, Azhar Toha, Siti Fauziah Mohd Romlay, Muhammad Rabani T Technology (General) Available hospital evacuation simulation models usually focus on the movement of the evacuees while ignoring the crucial behavioural factors of the evacuees’ which impact the simulation results. For instance, the issue of patient prioritization behaviour during evacuation simulation is often overlooked and oversimplified in these models. Furthermore, to control the movement of the evacuees, almost all these models utilize rule-based artificial intelligence to develop navigation systems, which sometimes do not guarantee realistic and optimal movement behaviour. This research aims to address these problems by modelling feasible and novel solutions. In this research, we propose to develop a hospital evacuation simulation model which utilizes a hybrid of fuzzy logic and reinforcement learning to simulate assisted hospital evacuation using the Unity3D game engine. We propose a novel and effective approach to model patient prioritization using a fuzzy logic controller; a reinforcement learning based navigation system to tackle the issues related to the rule-based navigation system by proposing novel reward formulation, observation formulation, action formulation and training procedure. The results and findings exhibited by the proposed model are found to be in line with the available literature. For instance, available literature suggests that an increased number of patients increases the evacuation time, and an increased number of staff or exits decreases the evacuation time. The proposed model also demonstrates similar findings. Moreover, the proposed navigation system is found to take a ‘‘near shortest distance’’ to reach the target as the mean difference between ‘‘shortest vector distance’’ and ‘‘distance covered’’ is approximately 1.73 m. The proposed simulation model simulates the repeated patient collection more realistically and can be used to estimate the Required Safe Egress Time, which enables the establishment of safety performance levels. The evacuation performance of different scenarios can be compared using the proposed model. This research can play a vital role in future developments of hospital evacuation simulation models. Springer Nature 2024-01-19 Article PeerReviewed application/pdf en http://irep.iium.edu.my/110540/1/NCAAIntiaz.pdf application/pdf en http://irep.iium.edu.my/110540/7/110540_Modelling%20and%20simulation%20of%20assisted%20hospital_SCOPUS.pdf Mohammad Abir, Intiaz and Mohd Ibrahim, Azhar and Toha, Siti Fauziah and Mohd Romlay, Muhammad Rabani (2024) Modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach. Neural Computing and Applications. pp. 1-30. ISSN 0941-0643 E-ISSN 1433-3058 https://link.springer.com/article/10.1007/s00521-023-09389-w 10.1007/s00521-023-09389-w
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic T Technology (General)
spellingShingle T Technology (General)
Mohammad Abir, Intiaz
Mohd Ibrahim, Azhar
Toha, Siti Fauziah
Mohd Romlay, Muhammad Rabani
Modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach
description Available hospital evacuation simulation models usually focus on the movement of the evacuees while ignoring the crucial behavioural factors of the evacuees’ which impact the simulation results. For instance, the issue of patient prioritization behaviour during evacuation simulation is often overlooked and oversimplified in these models. Furthermore, to control the movement of the evacuees, almost all these models utilize rule-based artificial intelligence to develop navigation systems, which sometimes do not guarantee realistic and optimal movement behaviour. This research aims to address these problems by modelling feasible and novel solutions. In this research, we propose to develop a hospital evacuation simulation model which utilizes a hybrid of fuzzy logic and reinforcement learning to simulate assisted hospital evacuation using the Unity3D game engine. We propose a novel and effective approach to model patient prioritization using a fuzzy logic controller; a reinforcement learning based navigation system to tackle the issues related to the rule-based navigation system by proposing novel reward formulation, observation formulation, action formulation and training procedure. The results and findings exhibited by the proposed model are found to be in line with the available literature. For instance, available literature suggests that an increased number of patients increases the evacuation time, and an increased number of staff or exits decreases the evacuation time. The proposed model also demonstrates similar findings. Moreover, the proposed navigation system is found to take a ‘‘near shortest distance’’ to reach the target as the mean difference between ‘‘shortest vector distance’’ and ‘‘distance covered’’ is approximately 1.73 m. The proposed simulation model simulates the repeated patient collection more realistically and can be used to estimate the Required Safe Egress Time, which enables the establishment of safety performance levels. The evacuation performance of different scenarios can be compared using the proposed model. This research can play a vital role in future developments of hospital evacuation simulation models.
format Article
author Mohammad Abir, Intiaz
Mohd Ibrahim, Azhar
Toha, Siti Fauziah
Mohd Romlay, Muhammad Rabani
author_facet Mohammad Abir, Intiaz
Mohd Ibrahim, Azhar
Toha, Siti Fauziah
Mohd Romlay, Muhammad Rabani
author_sort Mohammad Abir, Intiaz
title Modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach
title_short Modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach
title_full Modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach
title_fullStr Modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach
title_full_unstemmed Modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach
title_sort modelling and simulation of assisted hospital evacuation using fuzzy-reinforcement learning based modelling approach
publisher Springer Nature
publishDate 2024
url http://irep.iium.edu.my/110540/1/NCAAIntiaz.pdf
http://irep.iium.edu.my/110540/7/110540_Modelling%20and%20simulation%20of%20assisted%20hospital_SCOPUS.pdf
http://irep.iium.edu.my/110540/
https://link.springer.com/article/10.1007/s00521-023-09389-w
_version_ 1805880571601092608
score 13.211869

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