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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">PBES</journal-id><journal-title-group><journal-title>Proceedings of Business and Economic Studies</journal-title></journal-title-group><issn>2209-2641</issn><eissn>2209-265X</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/pbes.v7i1.6072</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>An Emergency Plan Selection Method for Emergency Latency</title><url>https://artdesignp.com/journal/PBES/7/1/10.26689/pbes.v7i1.6072</url><author>LiuHuajun</author><pub-date pub-type="publication-year"><year>2024</year></pub-date><volume>7</volume><issue>1</issue><history><date date-type="pub"><published-time>2024-02-25</published-time></date></history><abstract>Drawing upon a characteristic analysis of the latency period in emergencies, this paper proposes an emergency plan selection method based on interval language variables and information entropy to address the challenge of acquiring critical information during this crucial stage. Initially, decision-makers employ interval language variables to express the preference information regarding emergency plans, while also introducing an enhanced information entropy theory to derive the weight coefficients of key indicators. Subsequently, the weighted arithmetic average operator of interval language is applied twice to aggregate the preference information alongside the relative importance of decision-makers and the weight coefficients of key indicators. Finally, the ranking coefficients of each emergency plan are sorted to determine the optimal scheme. The feasibility and effectiveness of this method are demonstrated through a case study involving the selection of an emergency plan for a flood disaster in a specific location.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Abdulla MB, Costa AL, Sousa RL, 2018, Probabilistic Identification of Subsurface Gypsum Geohazards Using Artificial Neural Networks. Neural Computing and Applications, 29: 1377–1391. https://doi.org/10.1007/s00521-016-2655-3</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Zhang L, Chettupuzha AAJ, Chen H, et al., 2017, Fuzzy Cognitive Maps Enabled Root Cause Analysis in Complex Projects. 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