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Resilience Design of Healthcare Resources Supply Network Based on Self-Organized Criticality
The healthcare resources supply network design for resilience is an effective way to deal with uncertainty disruption. In this article we propose a model of supply network self-organization evolution, and establish self-organized criticality as a cause of cascade failure. Our main purpose is to keep...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7551799/ https://www.ncbi.nlm.nih.gov/pubmed/32751467 http://dx.doi.org/10.3390/healthcare8030245 |
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author | Geng, Liang Xiao, Renbin Chen, Jie |
author_facet | Geng, Liang Xiao, Renbin Chen, Jie |
author_sort | Geng, Liang |
collection | PubMed |
description | The healthcare resources supply network design for resilience is an effective way to deal with uncertainty disruption. In this article we propose a model of supply network self-organization evolution, and establish self-organized criticality as a cause of cascade failure. Our main purpose is to keep the system in a resilient range, i.e., critical state. A network structural design with smaller degree distribution exponent can achieve better absorptive capacity at macro level. An interactive rule design with extremal optimization has better adaptive capacity at micro level. Using macro statistic and indicator micro performance indicator, we demonstrate that our design can slow the development to a supercritical state and can improve the resilience of the supply network. |
format | Online Article Text |
id | pubmed-7551799 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75517992020-10-14 Resilience Design of Healthcare Resources Supply Network Based on Self-Organized Criticality Geng, Liang Xiao, Renbin Chen, Jie Healthcare (Basel) Article The healthcare resources supply network design for resilience is an effective way to deal with uncertainty disruption. In this article we propose a model of supply network self-organization evolution, and establish self-organized criticality as a cause of cascade failure. Our main purpose is to keep the system in a resilient range, i.e., critical state. A network structural design with smaller degree distribution exponent can achieve better absorptive capacity at macro level. An interactive rule design with extremal optimization has better adaptive capacity at micro level. Using macro statistic and indicator micro performance indicator, we demonstrate that our design can slow the development to a supercritical state and can improve the resilience of the supply network. MDPI 2020-07-30 /pmc/articles/PMC7551799/ /pubmed/32751467 http://dx.doi.org/10.3390/healthcare8030245 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Geng, Liang Xiao, Renbin Chen, Jie Resilience Design of Healthcare Resources Supply Network Based on Self-Organized Criticality |
title | Resilience Design of Healthcare Resources Supply Network Based on Self-Organized Criticality |
title_full | Resilience Design of Healthcare Resources Supply Network Based on Self-Organized Criticality |
title_fullStr | Resilience Design of Healthcare Resources Supply Network Based on Self-Organized Criticality |
title_full_unstemmed | Resilience Design of Healthcare Resources Supply Network Based on Self-Organized Criticality |
title_short | Resilience Design of Healthcare Resources Supply Network Based on Self-Organized Criticality |
title_sort | resilience design of healthcare resources supply network based on self-organized criticality |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7551799/ https://www.ncbi.nlm.nih.gov/pubmed/32751467 http://dx.doi.org/10.3390/healthcare8030245 |
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