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Combinatorial Sequences for Disaster Scenario Generation

Training exercises are an important tool in crisis management, as they can assist in a multitude of tasks, such as planning pre-crisis resource requirements and allocation, response planning and help train emergency personnel for actual crises. To be effective, the exercises have to utilize well con...

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Autores principales: Garn, Bernhard, Kieseberg, Klaus, Schreiber, Dominik, Simos, Dimitris E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244859/
http://dx.doi.org/10.1007/s43069-023-00225-4
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author Garn, Bernhard
Kieseberg, Klaus
Schreiber, Dominik
Simos, Dimitris E.
author_facet Garn, Bernhard
Kieseberg, Klaus
Schreiber, Dominik
Simos, Dimitris E.
author_sort Garn, Bernhard
collection PubMed
description Training exercises are an important tool in crisis management, as they can assist in a multitude of tasks, such as planning pre-crisis resource requirements and allocation, response planning and help train emergency personnel for actual crises. To be effective, the exercises have to utilize well constructed scenarios and be able to replicate certain characteristics of a crisis situation. In this paper, we propose a conceptual mathematical modeling approach for the automated generation of scenarios for disaster exercises via certain combinatorial sequence structures. The derived scenarios within an exercise collectively fulfill different notions of combinatorial sequence coverage, thereby providing the means to test existing response strategies for deficiencies as well as to train emergency personnel for their ability to handle different arrangements of events. This guaranteed diversity by construction can be used as a basis to obtain quantitative assurance statements when these scenarios have been successfully mastered by participants in exercises. We illustrate our proposed approach utilizing two different combinatorial structures for two example disasters.
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spelling pubmed-102448592023-06-08 Combinatorial Sequences for Disaster Scenario Generation Garn, Bernhard Kieseberg, Klaus Schreiber, Dominik Simos, Dimitris E. Oper. Res. Forum Original Research Training exercises are an important tool in crisis management, as they can assist in a multitude of tasks, such as planning pre-crisis resource requirements and allocation, response planning and help train emergency personnel for actual crises. To be effective, the exercises have to utilize well constructed scenarios and be able to replicate certain characteristics of a crisis situation. In this paper, we propose a conceptual mathematical modeling approach for the automated generation of scenarios for disaster exercises via certain combinatorial sequence structures. The derived scenarios within an exercise collectively fulfill different notions of combinatorial sequence coverage, thereby providing the means to test existing response strategies for deficiencies as well as to train emergency personnel for their ability to handle different arrangements of events. This guaranteed diversity by construction can be used as a basis to obtain quantitative assurance statements when these scenarios have been successfully mastered by participants in exercises. We illustrate our proposed approach utilizing two different combinatorial structures for two example disasters. Springer International Publishing 2023-06-07 2023 /pmc/articles/PMC10244859/ http://dx.doi.org/10.1007/s43069-023-00225-4 Text en © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Research
Garn, Bernhard
Kieseberg, Klaus
Schreiber, Dominik
Simos, Dimitris E.
Combinatorial Sequences for Disaster Scenario Generation
title Combinatorial Sequences for Disaster Scenario Generation
title_full Combinatorial Sequences for Disaster Scenario Generation
title_fullStr Combinatorial Sequences for Disaster Scenario Generation
title_full_unstemmed Combinatorial Sequences for Disaster Scenario Generation
title_short Combinatorial Sequences for Disaster Scenario Generation
title_sort combinatorial sequences for disaster scenario generation
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10244859/
http://dx.doi.org/10.1007/s43069-023-00225-4
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