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<dc:title xml:lang="pl"><![CDATA[Bridging stochastic, robust, and neutrosophic paradigms for hurricane evacuation under compound uncertainty: A six-layer methodological synthesis]]></dc:title>
<dc:creator><![CDATA[Fernández-Fernández, Yasmany]]></dc:creator>
<dc:creator><![CDATA[Allende Alonso, Sira M.]]></dc:creator>
<dc:creator><![CDATA[Miranda Pérez, Ridelio]]></dc:creator>
<dc:creator><![CDATA[Bouza Allende, Gemayqzel]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[hurricane evacuation]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[robust optimization]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[stochastic programming]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[neutrosophic uncertainty sets]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[hierarchical decomposition]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[Hurricane evacuation planning under uncertainty requires integrating decisions across multiple spatial and temporal scales, yet the literature addresses demand stochasticity, temporal robustness, and computational tractability largely in isolation.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[This paper develops a unified optimization framework organized around six progressive layers: meteorological scenario generation, deterministic and stochastic network flow planning, vehicle scheduling with origin-based decomposition, classical robust temporal protection, neutrosophic adaptive robust optimization with scenario-anchored uncertainty sets, and computational complexity characterization.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[An integrated critical review of 337 Scopus-indexed publications (2022-2026) identifies persistent methodological gaps that motivate each architectural layer. The framework couples two-stage stochastic programming with budget-constrained robust optimization, affine decision rules, and neutrosophic indeterminacy calibration within a single operational pipeline.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[Validation on a georeferenced Caribbean network demonstrates a 94% reduction in binary variables through origin-based decomposition and end-to-end execution in under four minutes, with a sensitivity analysis showing that neutrosophic budget calibration reduces the price of robustness from 24.8% to 2.8%.]]></dc:description>
<dc:publisher><![CDATA[Zielona Góra: Uniwersytet Zielonogórski]]></dc:publisher>
<dc:contributor><![CDATA[Okarma, Krzysztof - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Skrzypczyński, Piotr - ed.]]></dc:contributor>
<dc:contributor><![CDATA[Stefanowski, Jerzy - ed.]]></dc:contributor>
<dc:date><![CDATA[2026]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/Content/98927/amcs-2026-0034.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/dlibra/publication/115180/edition/98927/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:98927]]></dc:identifier>
<dc:source xml:lang="pl"><![CDATA[AMCS, volume 36, number 3 (2026)]]></dc:source>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:115180]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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