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Статті в журналах з теми "Shelter location under uncertainty":
Bayram, Vedat, and Hande Yaman. "Shelter Location and Evacuation Route Assignment Under Uncertainty: A Benders Decomposition Approach." Transportation Science 52, no. 2 (March 2018): 416–36. http://dx.doi.org/10.1287/trsc.2017.0762.
Xiang, Xiaoshu, Ye Tian, Jianhua Xiao, and Xingyi Zhang. "A Clustering-Based Surrogate-Assisted Multiobjective Evolutionary Algorithm for Shelter Location Problem Under Uncertainty of Road Networks." IEEE Transactions on Industrial Informatics 16, no. 12 (December 2020): 7544–55. http://dx.doi.org/10.1109/tii.2019.2962137.
Celik, Erkan. "Analyzing the Shelter Site Selection Criteria for Disaster Preparedness Using Best–Worst Method under Interval Type-2 Fuzzy Sets." Sustainability 16, no. 5 (March 4, 2024): 2127. http://dx.doi.org/10.3390/su16052127.
Trivedi, Ashish, and Amol Singh. "A multi-objective approach for locating temporary shelters under damage uncertainty." International Journal of Operational Research 38, no. 1 (2020): 31. http://dx.doi.org/10.1504/ijor.2020.10027955.
Trivedi, Ashish, and Amol Singh. "A multi-objective approach for locating temporary shelters under damage uncertainty." International Journal of Operational Research 38, no. 1 (2020): 31. http://dx.doi.org/10.1504/ijor.2020.106359.
Galemba, Rebecca, Katie Dingeman, Kaelyn DeVries, and Yvette Servin. "Paradoxes of Protection: Compassionate Repression at the Mexico–Guatemala Border." Journal on Migration and Human Security 7, no. 3 (July 29, 2019): 62–78. http://dx.doi.org/10.1177/2331502419862239.
Ozbay, Eren, Özlem Çavuş, and Bahar Y. Kara. "Shelter site location under multi-hazard scenarios." Computers & Operations Research 106 (June 2019): 102–18. http://dx.doi.org/10.1016/j.cor.2019.02.008.
Bayram, Vedat, and Hande Yaman. "A stochastic programming approach for Shelter location and evacuation planning." RAIRO - Operations Research 52, no. 3 (July 2018): 779–805. http://dx.doi.org/10.1051/ro/2017046.
Alumur, Sibel A., Stefan Nickel, and Francisco Saldanha-da-Gama. "Hub location under uncertainty." Transportation Research Part B: Methodological 46, no. 4 (May 2012): 529–43. http://dx.doi.org/10.1016/j.trb.2011.11.006.
Li, Anna C. Y., Linda Nozick, Ningxiong Xu, and Rachel Davidson. "Shelter location and transportation planning under hurricane conditions." Transportation Research Part E: Logistics and Transportation Review 48, no. 4 (July 2012): 715–29. http://dx.doi.org/10.1016/j.tre.2011.12.004.
Дисертації з теми "Shelter location under uncertainty":
Resseguier, Valentin. "Mixing and fluid dynamics under location uncertainty." Thesis, Rennes 1, 2017. http://www.theses.fr/2017REN1S004/document.
This thesis develops, analyzes and demonstrates several valuable applications of randomized fluid dynamics models referred to as under location uncertainty. The velocity is decomposed between large-scale components and random time-uncorrelated small-scale components. This assumption leads to a modification of the material derivative and hence of every fluid dynamics models. Through the thesis, the mixing induced by deterministic low-resolution flows is also investigated. We first applied that decomposition to reduced order models (ROM). The fluid velocity is expressed on a finite-dimensional basis and its evolution law is projected onto each of these modes. We derive two types of ROMs of Navier-Stokes equations. A deterministic LES-like model is able to stabilize ROMs and to better analyze the influence of the residual velocity on the resolved component. The random one additionally maintains the variability of stable modes and quantifies the model errors. We derive random versions of several geophysical models. We numerically study the transport under location uncertainty through a simplified one. A single realization of our model better retrieves the small-scale tracer structures than a deterministic simulation. Furthermore, a small ensemble of simulations accurately predicts and describes the extreme events, the bifurcations as well as the amplitude and the position of the ensemble errors. Another of our derived simplified model quantifies the frontolysis and the frontogenesis in the upper ocean. This thesis also studied the mixing of tracers generated by smooth fluid flows, after a finite time. We propose a simple model to describe the stretching as well as the spatial and spectral structures of advected tracers. With a toy flow but also with satellite images, we apply our model to locally and globally describe the mixing, specify the advection time and the filter width of the Lagrangian advection method, as well as the turbulent diffusivity in numerical simulations
Haddad, Marcel Adonis. "Nouveaux modèles robustes et probabilistes pour la localisation d'abris dans un contexte de feux de forêt." Electronic Thesis or Diss., Université Paris sciences et lettres, 2020. http://www.theses.fr/2020UPSLD021.
The location of shelters in different areas threatened by wildfires is one of the possible ways to reduce fatalities in acontext of an increasing number of catastrophic and severe forest fires. The problem is basically to locate p sheltersminimizing the maximum distance people will have to cover to reach the closest accessible shelter in case of fire. Thelandscape is divided in zones and is modeled as an edge-weighted graph with vertices corresponding to zones andedges corresponding to direct connections between two adjacent zones. Each scenario corresponds to a fire outbreak ona single zone (i.e., on a vertex) with the main consequence of modifying evacuation paths in two ways. First, an evacuationpath cannot pass through the vertex on fire. Second, the fact that someone close to the fire may have limited choice, ormay not take rational decisions, when selecting a direction to escape is modeled using a new kind of evacuation strategy.This evacuation strategy, called Under Pressure, induces particular evacuation distances which render our model specific.We propose two problems with this model: the Robust p-Center Under Pressure problem and the Probabilistic p-CenterUnder Pressure problem. First we prove hardness results for both problems on relevant classes of graphs for our context.In addition, we propose polynomial exact algorithms on simple classes of graphs and we develop mathematical algorithmsbased on integer linear programming
Grannan, Benjamin. "Dispatch, Delivery, and Location Logistics for the Aeromedical Evacuation of Time-Sensitive Military Casualties Under Uncertainty." VCU Scholars Compass, 2014. http://scholarscompass.vcu.edu/etd/3536.
Kurdi, Mohammad H. "Robust multicriteria optimization of surface location error and material removal rate in high-speed milling under uncertainty." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0011626.
Theodoridis, Constantinos. "Strategic retail location decision-making under uncertainty : an application of complexity theory in the Greek retailing sector." Thesis, Manchester Metropolitan University, 2014. http://e-space.mmu.ac.uk/652/.
Meeyai, Sutthipong. "A Hybrid Approach for The Design of Facility Location and Supply Chain Network Under Supply and Demand Uncertainty: A Systematic Review." Thesis, Cranfield University, 2009. http://dspace.lib.cranfield.ac.uk/handle/1826/4673.
Ashuri, Baabak. "A Real Options Approach to Modeling Investments in Competitive, Dynamic Retail Markets." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24608.
Siddiq, Auyon. "Robust Facility Location under Demand Location Uncertainty." Thesis, 2013. http://hdl.handle.net/1807/42932.
Hajizadeh, Saffar Iman. "Problems in Supply Chain Location and Inventory under Uncertainty." Thesis, 2010. http://hdl.handle.net/1807/24764.
Marques, Maria do Céu Lourenço. "Dynamic location problems under uncertainty: models and optimization techniques." Doctoral thesis, 2015. http://hdl.handle.net/10316/28525.
This thesis is devoted to mathematical modelling and solution techniques for dynamic facility location problems under uncertainty. The uncertainty regarding the evolution of important problems' parameters along the planning horizon, such as setup and assignment costs, as well as level or location of demand, is explicitly incorporated into the dynamic models through a finite and discrete set of possible scenarios. In the present work we first propose a two-stage stochastic model for the uncapacitated problem. The first decisions to be made are the strategic ones, where and when to locate the facilities throughout the planning horizon. The second-stage decisions refer to the assignment of the existing customers to the open facilities over the whole planning horizon under each possible scenario. As opposite to location decisions, that must be made here and now and should be valid for all possible future scenarios, assignment can be decided after the uncertainty has been resolved and thus can be adjusted in each time period to each possible scenario. The objective is to find a solution that minimizes the expected total cost over all possible scenarios. This model is then extended to other situations, recognizing that other features should be included in the mathematical model to be able to generate other possible solutions. A set of robust constraints is incorporated into that model, that in spite of restricting the set of admissible solutions, it offers more informed and robust solutions under uncertainty. A multi-objective problem wherein each scenario gives rise to an objective is then developed, and relations with other known problems are established as well. For this latter model, requirements about scenarios probabilities or risk profiles are dropped. Within this context, it is emphasized that the Decision Maker will have a better picture of the compromises that exist among the possible scenarios. In terms of models, we conclude with several extensions considering capacitated facilities. The possibility of unmet demand appears naturally in this class of problems, giving rise to other interesting and challenging questions. We propose and discuss both mono and multi-objective approaches. We proceed with the description of the solution techniques that have been developed to tackle the uncapacitated problems. First we present a primal-dual heuristic approach inspired on classical works and a branch&bound scheme integrating this same heuristic. Afterwards, a Lagrangean relaxation approach developed to tackle the problem with robust constraints is detailed. The calculation of non-dominated solutions for the multi-objective problem is discussed and illustrated. Finally, as the models and algorithms were tested over sets of randomly generated problems, the computational experiments and results obtained are provided including comparisons with general solvers. The results of this work aim to help Decision Makers in the difficult process of decision making when dealing with location problems under uncertainty, and thus should be interpreted as decision support tools.
Esta tese versa sobre modelação matemática e algoritmos de resolução de problemas de localização dinâmica em contextos de incerteza. A incerteza acerca de como importantes parâmetros dos problemas irão evoluir ao longo do tempo, tais como custos de instalação de serviços e de afetação, localização ou nível da procura, é explicitamente incorporada nos modelos dinâmicos através de um conjunto finito e discreto de cenários. Na presente dissertação, propomos em primeiro lugar um modelo estocástico de duas fases para o problema de localização sem restrições de capacidades. As primeiras decisões a serem tomadas são as estratégicas, onde e quando localizar os serviços ao longo do horizonte temporal. As decisões de segunda fase referem-se à afetação dos clientes com procura aos serviços abertos ao longo do horizonte temporal para todos os cenários possíveis. Ao contrário das decisões de localização, tomadas no presente e válidas para todos os futuros possíveis, as decisões de afetação podem ser tomadas após a realização da incerteza e ajustadas em cada período temporal a cada cenário. O objetivo do problema é encontrar uma solução que minimize o custo total esperado para todos os cenários possíveis. Este modelo é depois alargado a outras situações, reconhecendo-se que outras características devem ser incluídas no modelo de modo a gerar outras soluções para o problema. Um conjunto de restrições de robustez é incorporado no modelo que, apesar de restringir o conjunto de soluções admissíveis, oferece soluções mais informadas e robustas em situações de incerteza. Um problema multi-objetivo em que cada cenário origina um objetivo é depois apresentado, assim como relações com outros problemas conhecidos. Requisitos acerca das probabilidades associadas aos cenários ou acerca de perfis de risco são desnecessários. É ainda sublinhado que neste contexto o Agente de Decisão terá um melhor retrato dos compromissos existentes entre os possíveis cenários. Em termos de modelos, concluímos com várias extensões considerando serviços com capacidades limitadas. A possibilidade de procura insatisfeita surge naturalmente nesta classe de problemas, dando lugar a outras interessantes e desafiantes questões. Propomos e discutimos abordagens mono e multi-objetivo. Procedemos à descrição dos algoritmos construídos para resolução dos problemas sem restrições de capacidades. Apresentamos uma heurística primal-dual inspirada em abordagens clássicas e um algoritmo branch&bound que integra aquela heurística. Uma técnica usando relaxação Lagrangeana é depois detalhada para resolução do problema com as restrições de robustez. O cálculo de soluções não dominadas para o problema multi-objetivo é discutido e ilustrado com um exemplo. Finalmente, como tanto os modelos como os algoritmos foram testados com instâncias geradas aleatoriamente, as experiências e resultados computacionais são apresentados, incluindo comparações com general solvers. Os resultados deste trabalho pretendem ajudar os Agentes de Decisão no difícil processo de decisão perante problemas de localização em contexto de incerteza, e assim devem ser interpretados como ferramentas de apoio à decisão.
Книги з теми "Shelter location under uncertainty":
Saldanha-da-Gama, Francisco, and Shuming Wang. Facility Location Under Uncertainty. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-55927-3.
Частини книг з теми "Shelter location under uncertainty":
Correia, Isabel, and Francisco Saldanha-da-Gama. "Facility Location Under Uncertainty." In Location Science, 185–213. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32177-2_8.
Correia, Isabel, and Francisco Saldanha da Gama. "Facility Location Under Uncertainty." In Location Science, 177–203. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-13111-5_8.
Patan, Maciej. "Sensor Location under Parametric and Location Uncertainty." In Optimal Sensor Networks Scheduling in Identification of Distributed Parameter Systems, 183–206. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28230-0_7.
Taherkhani, Gita, and Sibel A. Alumur. "Hub Location Models Under Uncertainty." In International Series in Operations Research & Management Science, 337–54. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32338-6_13.
Tissot, Gilles, Étienne Mémin, and Quentin Jamet. "Stochastic Compressible Navier–Stokes Equations Under Location Uncertainty." In Mathematics of Planet Earth, 293–319. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40094-0_14.
Tucciarone, Francesco L., Etienne Mémin, and Long Li. "Primitive Equations Under Location Uncertainty: Analytical Description and Model Development." In Mathematics of Planet Earth, 287–300. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-18988-3_18.
Malmi, Eric, Arno Solin, and Aristides Gionis. "The Blind Leading the Blind: Network-Based Location Estimation Under Uncertainty." In Machine Learning and Knowledge Discovery in Databases, 406–21. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23525-7_25.
Figueroa–García, Juan Carlos, Carlos Franco, and Roman Neruda. "An Optimization Model for Location-Allocation of Health Services Under Uncertainty." In Computational Intelligence Methodologies Applied to Sustainable Development Goals, 97–108. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97344-5_7.
Dias, Joana M., and Maria do Céu Marques. "A Multiobjective Approach for a Dynamic Simple Plant Location Problem under Uncertainty." In Computational Science and Its Applications – ICCSA 2014, 60–75. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09129-7_5.
Liu, Yang, Yun Yuan, Yi Chen, Lingxiao Ruan, and Hao Pang. "A Chance Constrained Goal Programming Model for Location-Routing Problem Under Uncertainty." In LISS 2013, 105–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40660-7_15.
Тези доповідей конференцій з теми "Shelter location under uncertainty":
Yardimci, Y., and J. A. Cadzow. "Direction-of-arrival estimation under sensor location uncertainty." In Proceedings of ICASSP '93. IEEE, 1993. http://dx.doi.org/10.1109/icassp.1993.319657.
Thi, Nguyen Truong, Phan Thi Kim Phung, and Tran Thi Tham. "Optimizing Warehouse Storage Location Assignment Under Demand Uncertainty." In 2020 5th International Conference on Green Technology and Sustainable Development (GTSD). IEEE, 2020. http://dx.doi.org/10.1109/gtsd50082.2020.9303113.
Qing Ye, Jianshe Song, Zhenglei Yang, and Lianfeng Wang. "Emergency vehicle location model and algorithm under uncertainty." In 2011 2nd IEEE International Conference on Emergency Management and Management Sciences (ICEMMS). IEEE, 2011. http://dx.doi.org/10.1109/icemms.2011.6015604.
Vitale, Christian, Panayiotis Kolios, and Georgios Ellinas. "Intersection Crossing with Connected Autonomous Vehicles under Location Uncertainty." In GLOBECOM 2020 - 2020 IEEE Global Communications Conference. IEEE, 2020. http://dx.doi.org/10.1109/globecom42002.2020.9322403.
Wu, Pengcheng, Junfei Xie, and Jun Chen. "Safe Path Planning for Unmanned Aerial Vehicle under Location Uncertainty." In 2020 IEEE 16th International Conference on Control & Automation (ICCA). IEEE, 2020. http://dx.doi.org/10.1109/icca51439.2020.9264542.
Liu, Liping, and Zipeng Yi. "Robust Model for Multimodal Location of the Hazmat under Uncertainty." In 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016). Paris, France: Atlantis Press, 2016. http://dx.doi.org/10.2991/aiie-16.2016.5.
Ren, Ming_ming, Chao Yang, and Bo He. "An Integrated Model and Algorithm for Facility Location under Uncertainty." In Third International Conference on Natural Computation (ICNC 2007). IEEE, 2007. http://dx.doi.org/10.1109/icnc.2007.228.
Aanonsen, S. I., A. L. Eide, L. Holden, and J. O. Aasen. "Optimizing Reservoir Performance Under Uncertainty with Application to Well Location." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 1995. http://dx.doi.org/10.2118/30710-ms.
Zhai, Junda, and Guangquan Lu. "Uncertainty Estimation of Location Information under Vehicle-Vehicle Cooperative Control." In 18th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2018. http://dx.doi.org/10.1061/9780784481523.007.
Ranasinghe, Champika, Nicholas Schiestel, and Christian Kray. "Visualising Location Uncertainty to Support Navigation under Degraded GPS Signals." In MobileHCI '19: 21st International Conference on Human-Computer Interaction with Mobile Devices and Services. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3338286.3340128.
Звіти організацій з теми "Shelter location under uncertainty":
Xepapadeas, Anastasios. Environmental Policy and Firm Behavior: Abatement Investment and Location Decisions Under Uncertainty and Irreversibility. Cambridge, MA: National Bureau of Economic Research, August 1999. http://dx.doi.org/10.3386/t0243.
Kingston, A. W., A. Mort, C. Deblonde, and O H Ardakani. Hydrogen sulfide (H2S) distribution in the Triassic Montney Formation of the Western Canadian Sedimentary Basin. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/329797.
Liu and Nixon. L52305 Probabilistic Analysis of Pipeline Uplift Resistance. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), June 2010. http://dx.doi.org/10.55274/r0000002.