Dissertations / Theses on the topic 'Shelter location under uncertainty'
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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.
Shieh, Yuh-Woei, and 謝育偉. "Price Uncertainty and Production-Location Theory Under Free Entry Oligopoly." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/05453053387981692331.
Chun-LinHo and 何俊霖. "A two-step approach for location allocation problems under an uncertainty environment." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/54813751726468455855.
國立成功大學
工業與資訊管理學系碩博士班
101
Facility Location Allocation (FLA) problems involve many qualitative factors like environment, political and quantitative factors such as setup and transportation costs. In order to deal with this problem, decision makers can consider many different criteria using an approach called Multiple Criteria Decision Making (MCDM). According to different application methods for scholars, MCDM can be divided into Multiple Attribute Decision Making (MADM) and Multiple Objective Decision Making (MODM). In addition, when decision makers deal with qualitative criteria, subjective opinions often exist, and crisp value cannot express expert opinions completely. Thus, this study builds a model in a fuzzy environment that will handle the MCDM problem with fuzzy linguistic variables. The proposed model includes a preparation state, a first state and a second state. In the preparation state, experts identify criteria and alternatives and then divide the criteria into qualitative criteria and quantitative criteria. The purpose of the first state is to deal with the qualitative criteria. In this state, the experts give performance ratings to alternatives using linguistic variables and then apply Fuzzy MADM to obtain an assessment of each alternative. In the second state, we build a multi-objective model using fuzzy goal programming that takes into consideration the fuzzy relation and priorities among objectives, and then we determine the number and locations of facilities as well as the demand. We subsequently compare and analyze the results between two methodologies, suggesting that the model can be applied to solve the FLA problem and give suggestions to decision makers.
Lin, Hung-Chin, and 林宏晉. "A Study of the Location Selection for Urban Neighborhood Park Under Uncertainty Factors." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/93670345448160763585.
朝陽科技大學
建築及都市設計研究所
92
This thesis proposes a model for use in selecting the location for a park facility where an uncertainty population is taken as a factor. In other existing models, the uncertainty population factor is simplified by using a fixed value or a value based on existing but inaccurate data. These methods oversimplify the issue and do not reflect actual circumstances, thereby causing inaccurate decisions. The proposed model seeks to rectify this problem. Taiwan has already reached a certain level of development that the old models are no longer appropriate. The proposed model seeks to complement the current situation while taking into consideration the uncertainty population factor. In the research conducted for the creation of the proposed model, detailed reviews of existing location theories, Genetic Algorithm, Monte Carlo simulation and Simulation Optimization have been made. An application regression model is used to make a projection on the uncertainty population and consideration to the uncertainty distance of travel. The Monte Carlo simulation is used for risk estimation. Integrate P-median concept of location theory to build an urban neighborhood park location selection basic model under uncertainty factors. Finally, the use of Genetic Algorithm in RISKOptimizer effectively shortens the computation time that would generate the optimum results in the selection of a location for the park facility. A case study has been made using the proposed model in selecting a location for a park facility in the West district of Taichung City in Taiwan. The proposed model considered the uncertain population of the neighborhood in the future and was able to generate results that reflected the actual circumstance more than the existing models.
張書銘. "Distribution system design:a warehouse location with two-level routing problem under demand uncertainty." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/70973673916396704694.
(7407275), Todd Zhen. "Optimal Sensor Placement Problems Under Uncertainty: Models and Applications." Thesis, 2019.
Nur, Ramadhani, and Ramadhani Nur. "Collaborative Two-Echelon Location Routing Problem under Demand Uncertainty: A Case Study of Yogyakarta, Indonesia." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/8t38eq.
國立臺灣科技大學
工業管理系
107
A significant increase in urban population causes demand for daily needs to rise. To fulfill all the order request, logistic companies are required to be more responsive and flexible. To maximize their profit, each company will try to use its resources efficiently in distributing goods to customers. A conventional way is to dispatch more vehicles operating around the city center. Consequently, more logistic activities are expected to happen, which might be accompanied by the occurrence of freight transport issues, such as traffic congestion, excess emission, noise, and crashes. Enabling collaboration between logistic companies and setting up intermediate facilities (satellites) in the city can be considered as alternatives to overcome the problems above. Previous researches have claimed that the implementation of both strategies might help to reduce the volume of vehicles traveling around the urban area. Given the positive impacts, this study applies a collaborative strategy along with the use of intermediate facilities to solve logistical problems in the Special Region of Yogyakarta, as one of the most densely populated cities in Indonesia. However, in solving real-world problems, there are daily uncertainties that need to be considered, such as variability in demand. To capture demand variability, two-stage stochastic programming with recourse model is developed. This research proposes a mathematical programming model for collaborative two-echelon location routing problem under demand uncertainty and uses CPLEX to solve small instances. A simulated annealing algorithm is employed to solve bigger problems. To evaluate the performance of the collaborative strategy, a comparative study between collaborative and non-collaborative scenario is conducted. The result shows that a collaborative approach can reduce the total distribution cost along with the decrement in total traveled distances and the number of utilized resources. Moreover, several cost allocation mechanisms based on cooperative game theory are presented to give a better overview of collaborative logistics as well as provide more options to allocate the post-collaboration cost among companies involved.