Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/6750
Title: Crisis management training using discrete-event simulation and virtual reality techniques
Authors: Dr. KWOK Pak Ki, Alex 
Yan, Mian 
Chan, K. P. Bill 
Lau, Y. K. Henry 
Issue Date: 2019
Source: Computers & Industrial Engineering, Sept. 2019, vol. 135, pp. 711-722.
Journal: Computers & Industrial Engineering 
Abstract: Conducting emergency drills in an actual environment is workforce and resources intensive. Hence, organisations often hesitate to conduct emergency exercises frequently. Because of the limited number of opportunities to conduct drills in a year, the content of the emergency drills can only focus on common cases and exclude rare cases. This constraint also restricts the members of crisis response teams from exploring and verifying new methods for tackling a crisis. Therefore, this research uses information and communication technology (ICT), virtual reality (VR) and discrete-event simulation (DES) technologies to develop a hazard simulation system with the capability to recreate large scale and multi-agency emergency incidents that would be otherwise too costly, complex and dangerous to reproduce in the actual system. With this system, organisations can conduct emergency drills inside a virtual world having a close correspondence with their real physical apparition. This training method is called virtual collaborative simulation-based training (VCST). Two laboratory-based studies were conducted to examine user perceptions about the VCST method. A total of 60 university students majored in managerial-related subjects were enrolled, and the results showed that the proposed method appears to be a feasible approach for practising crisis management training. Further research is needed to verify the findings in larger samples and different populations.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/6750
ISSN: 0360-8352
DOI: 10.1016/j.cie.2019.06.035
Appears in Collections:Applied Data Science - Publication

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