Fernández-Fernández, Yasmany ; Allende Alonso, Sira M. ; Miranda Pérez, Ridelio ; Bouza Allende, Gemayqzel
Współtwórca:Okarma, Krzysztof - ed. ; Skrzypczyński, Piotr - ed. ; Stefanowski, Jerzy - ed.
Tytuł: Podtytuł: Tytuł publikacji grupowej: Temat i słowa kluczowe:hurricane evacuation ; robust optimization ; stochastic programming ; neutrosophic uncertainty sets ; hierarchical decomposition
Abstract: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. ; 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. ; 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. ; 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%.
Wydawca:Zielona Góra: Uniwersytet Zielonogórski
Data wydania: Typ zasobu: DOI: Strony: Źródło:AMCS, volume 36, number 3 (2026)
Jezyk: Licencja CC BY 4.0: Prawa do dysponowania publikacją: