To see the other types of publications on this topic, follow the link: Management under uncertainty.
Dissertations / Theses on the topic 'Management under uncertainty'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Management under uncertainty.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Kistenmacher, Martin. "Reservoir system management under uncertainty." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/49012.
Full textAbstract:
Reservoir systems are subject to several uncertainties that are the result of imperfect knowledge about system behavior and inputs. A major source of uncertainty arises from the inability to predict future inflows. Fortunately, it is often possible to generate probabilistic forecasts of inflow volumes in the form of probability density functions or ensembles. These inflow forecasts can be coupled with stochastic management models to determine reservoir release policies and provide stakeholders with meaningful information of upcoming system responses such as reservoir levels, releases, flood damage risks, hydropower production, water supply withdrawals, water quality conditions, navigation opportunities, and environmental flows, among others. This information on anticipated system responses is also expressed in the form of forecasts that must reliably represent the actual system behavior when it eventually occurs. The first part of this study presents an assessment methodology that can be used to determine the consistency of ensemble forecasts through the use of relative frequency histograms and minimum spanning trees (MST). This methodology is then used to assess a management model's ability to produce reliable ensemble forecasts. It was found that neglecting to account for hydrologic state variables and improperly modeling the finite management horizon decrease ensemble consistency. Several extensions to the existing management model are also developed and evaluated. The second portion of this study involves the management of the uncertainties in reservoir systems. Traditional management models only find management policies that optimize the expected values of system benefits or costs, thereby not allowing operators and stakeholders to explicitly explore issues related to uncertainty and risk management. A technique that can be used to derive management policies that produce desired probabilistic distributions of reservoir system outputs reflecting stakeholder preferences is developed. This technique can be embedded in a user-interactive framework that can be employed to evaluate the trade-offs and build consensus in multi-objective and multi-stakeholder systems. The methods developed in this dissertation are illustrated in case studies of real reservoir systems, including a seven-reservoir, multi-objective system in California's Central Valley.
2
Fonseca, Raquel João. "International portfolio management under uncertainty." Thesis, Imperial College London, 2011. http://hdl.handle.net/10044/1/7115.
Full textAbstract:
Although the consideration of foreign investments may have a positive impact on the overall market risk of the portfolio through diversi cation, it also adds a new source of uncertainty due to changes in the value of the currency. We investigate portfolio optimization models that account separately for the local asset returns and the currency returns, providing the investor with a full investment strategy. We tackle the uncertainty inherent to the estimation of the parameters with the aid of robust optimization techniques. We show how, by using appropriate assumptions regarding the formulation of the uncertainty sets, the original non-linear and non-convex models may be reformulated as second order cone or as semide nite programs. Additionally to the guarantees provided by robust optimization, we consider the use of hedging instruments such as forward contracts and options. The proposed hedging strategies are implemented from a portfolio perspective, and therefore do not depend on the individual value or behavior of any particular asset or currency. Hedging decisions are taken at the same time as investment decisions in a holistic approach to portfolio management. While dynamic decision making has traditionally been represented as scenario trees, these may become severely intractable and di cult to compute with an increasing number of time periods. We present an alternative approach to multiperiod international portfolio optimization based on an a ne dependence between the decision variables and the past returns. We add to our formulation the minimization of the worst case value-at-risk and show the close relationship with robust optimization. The proposed theoretical framework is supported by various numerical experiments with simulated and historical market data demonstrating its potential bene ts.
3
Baresel, Christian. "Environmental management of water systems under uncertainty." Doctoral thesis, Stockholm : Mark- och vattenteknik, Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4396.
Full text
4
Chahar, Kiran. "Revenue and order management under demand uncertainty." Connect to this title online, 2008. http://etd.lib.clemson.edu/documents/1219855173/.
Full text
5
Nartey, Mohammed Donkor, and Anyinka Nkongtenden Ndobegang. "SUPPLIER SELECTION UNDER UNCERTAINTY." Thesis, Jönköping University, Jönköping International Business School, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-1189.
Full textAbstract:
The role of purchasing in supply chain management has received and continues to receive increasing attention as the years go by. Purchasing enhances efficiency and competitiveness among other benefits but to realize these benefits it is imperative to select and maintain competent suppliers. However, many factors affect a firm’s ability to choose the right supplier. Uncertainty is an issue that has received great attention. It affects all functions of a company consequently affecting purchasing and supplier selection. This thesis seeks to provide an understanding of the supplier selection process and criteria under circumstances of uncertainty in the case where the potential supplier under evaluation is a newly created company. The authors try to find out if uncertainty varies with firm’s age and tested the suitability of existing criteria on the selection of newly created firms. They also sought ways by which uncertainty can be reduced.One of the realisations of this thesis is that there is a relationship between the characteristics or problems faced by new firms and uncertainty. Uncertainties created by new firms include lack of trust and commitment, inadequate finance, poor quality, unreliable delivery times, inadequate logistic technological capabilities. No new types or sources of uncertainty were discovered however, it was found that uncertainty was certainly higher when working with new firms. The criteria delivery, quality, cost/price, financial position and communication and technology were recognized as the commonly used criteria a fact confirmed from empirical results as well as in previous literature. However other criteria such as ISO certification, reliability, credibility, good references and product development were also identified. These criteria had existed before but did not receive the same attention in previous studies. This show that focus is shifting from solely relying on quantitative factors to include qualitative criteria. The study identified that some methods of minimising uncertainty could include detailed financial analyses, site visit, intensive verification, close relationships, ISO certification, good references and recommendations. It is worth noting that uncertainty cannot be entirely eliminated in all situations
6
Eskandari, Abdollah 1952. "Decision support system in watershed management under uncertainty." Diss., The University of Arizona, 1997. http://hdl.handle.net/10150/191213.
Full textAbstract:
Watershed ecosystems consist of numerous resources which have important environmental, social, cultural, and economic values. The mutual existence and interaction among different resources within the watershed ecosystem calls for a multiobjective watershed resources management analysis. These objectives are often uncertain since they are based on estimation and/or measurement data. Probabilistic methods or fuzzification are usually the methods used in modeling these uncertainties. Selection of the best decision alternative is based on using some Multiple Criterion Decision Making (MCDM) technique. Through simulation in this dissertation, we examine the probabilistic model to address the watershed management problem. In particular, the distance-based methods, which are the most frequently used MCDM techniques, are employed in the problem analysis. In most cases, several interest groups with conflicting preferences are willing to influence the final decision. In our study, a new method is suggested to incorporate their preference orders into the DM's final preference. The application of MCDM techniques combined with stochastic simulation and conflicting preference orders is new in the watershed management literature. Detailed analysis and comparison of the numerical results will help to decide on the suitability of the MCDM technique in watershed resources management. In particular, our numerical results indicate that in practical applications the best alternative selection is significantly influenced by the uncertainties in the payoff values. Hence, in situations where suitable data are available, our methodology is highly recommended.
7
Zheng, Yahong. "Supply chain management under availability & uncertainty constraints." Thesis, Ecole centrale de Lille, 2012. http://www.theses.fr/2012ECLI0019/document.
Full textAbstract:
Le management de la chaîne logistique concerne un large éventail d’activités. Nombreuses ceux qui ont un caractère incertain apportant souvent des conséquences inattendues. Malgré cela, l’incertitude est fréquemment non considérée dans la gestion de la chaîne logistique traditionnelle. En plus de l’incertitude, l’indisponibilité des ressources augmentera la complexité du problème. En prenons en compte les contraintes d’incertitude et de disponibilité nous étudions le management de la chaîne logistique selon différents aspects. Cette thèse représente une tentative de recherche afin d’aborder ce problème d’une façon systématique et complète et nous espérons que notre travail contribuera aux futurs travaux de recherche et sera utile aux gestionnaires de la chaîne logistique. Nous nous concentrons sur trois sources classiques de l’incertitude ; celle de la demande, celle la fabrication et celle liée à la distribution. Pour chaque source d’incertitude, nous analysons ses causes et ses impacts sur les performances de la chaîne logistique. L’incertitude est spécifiée dans des problèmes classiques concrets et des approches sont proposées pour les résoudre. Nous nous sommes également focalisés sur le problème bi-niveau de vendeur de journaux qui représente une chaîne logistique miniature, concerné par une double incertitude. Les méthodes utilisées offrent une bonne démonstration du traitement des variables incertaines dans les problèmes de décisionSupply chain management involves a wide range of activities. Among most of them, uncertainty exists inherently and always brings some consequence not expected. However, uncertainty is not considered much in conventional supply chain management. In the case where availability of resources is not what we expect, complexity of supply chain management increases. Taking constraints of uncertainty and availability into account, we aim to discuss supply chain management from different aspects. This thesis is an attempt of systematic and complete research from this point and we would like to offer some references to researchers and managers in supply chain. We focus on three classic sources of uncertainty: demand, manufacturing and distribution. For each source of uncertainty, we analyze its cause and its impact to the performance of the supply chain. Uncertainty is specified into concrete classic problem and an approach is proposed to solve it. Furthermore, bi-level newsboy problem as a miniature of supply chain, is focused under double uncertain environment. Treating uncertain variables is actually a treatment on operational level. The methods used offer good demonstration in treating uncertain variables in decision problems
8
Betrie, Getnet Dubale. "Risk management of acid rock drainage under uncertainty." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/51562.
Full textAbstract:
Acid rock drainage (ARD) is a major environmental problem that causes local and global pollution. ARD occurs when sulfide bearing materials are exposed to oxygen and water during mining activities. This reaction between sulfide and oxygen with the presence of water generates elevated metals and metalloids that may cause potential environmental and human health risks. The remediation costs of potentially acid-generating wastes at abandoned minesites are estimated to be over $20 billion in USA. The major objective of this research is to propose a risk management framework for ARD that can improve the prediction of ARD chemistry, assess and manage environmental and human health risks to guide decision-making under uncertainty.
The proposed framework consists of methodologies for filling in missing data, predict ARD chemistry, assess environmental risks, and manage risks of ARD. In the first methodology, missing values of ARD data are filled in using imputation algorithms that reduce loss of information and introduction of biases. After having the complete data, future ARD chemistry is predicted using machine learning techniques. The predictive uncertainty due to data, parameters and model is quantified using a probability bounds approach. Models are integrated using aggregation methods to reduce the uncertainty of the individual model. Case studies in minesites show that the developed methodology improves the prediction of future ARD chemistry under uncertainty.
For ecological and human health risks assessment of ARD, two methodologies are developed based on the fugacity and PHREEQC approaches. The fugacity and PHREEQC approaches are applied in minesites with limited and adequate hydrogeological information, respectively. Case studies in minesites show that these methodologies are useful to quantify ecological and human health risks in the mining industry. In addition, they quantify the associated uncertainties in the risk assessments using the probability bounds and fuzzy-probabilistic approaches.
For risk management of ARD, a methodology that uses multicriteria decision analysis (MCDA) and probabilistic approaches is developed. The methodology enables decision-makers to evaluate mitigation measures with various criteria, such as risk, cost, and technical feasibility, identifies the optimal mitigation measure, and quantifies the associated uncertainties in decision-making. In general, it is believed that the proposed framework enhances the decision-making ability of the mining industry under uncertainty and reduces environmental risks and remediation costs of managing ARD.Applied Science, Faculty of
Engineering, School of (Okanagan)
Graduate
9
Dasu, Sriram. "Manufacturing decisions under uncertainty : models and methodology." Thesis, Massachusetts Institute of Technology, 1988. http://hdl.handle.net/1721.1/26804.
Full text
10
Kim, Joocheol. "Stochastic programming approach to asset liability management under uncertainty." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/25324.
Full text
11
Chung, Gunhui. "Water Supply System Management Design and Optimization under Uncertainty." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/195506.
Full textAbstract:
Increasing population, diminishing supplies and variable climatic conditions can cause difficulties in meeting water demands. When this long range water supply plan is developed to cope with future water demand changes, accuracy and reliability are the two most important factors. To develop an accurate model, the water supply system has become more complicated and comprehensive structures. Future uncertainty also has been considered to improve system reliability as well as economic feasibility.In this study, a general large-scale water supply system that is comprised of modular components was developed in a dynamic simulation environment. Several possible scenarios were simulated in a realistic hypothetical system. In addition to water balances and quality analyses, construction and operation of system components costs were estimated for each scenario. One set of results demonstrates that construction of small-cluster decentralized wastewater treatment systems could be more economical than a centralized plant when communities are spatially scattered or located in steep areas.The Shuffled Frog Leaping Algorithm (SFLA), then, is used to minimize the total system cost of the general water supply system. Decisions are comprised of sizing decisions - pipe diameter, pump design capacity and head, canal capacity, and water/wastewater treatment capabilities - and flow allocations over the water supply network. An explicit representation of energy consumption cost for the operation is incorporated into the system in the optimization process of overall system cost. Although the study water supply systems included highly nonlinear terms in the objective function and constraints, a stochastic search algorithm was applied successfully to find optimal solutions that satisfied all the constraints for the study networks.Finally, a robust optimization approach was introduced into the design process of a water supply system as a framework to consider uncertainties of the correlated future data. The approach allows for the control of the degree of conservatism which is a crucial factor for the system reliabilities and economical feasibilities. The system stability is guaranteed under the most uncertain condition and it was found that the water supply system with uncertainty can be a useful tool to assist decision makers to develop future water supply schemes.
12
Chang, Minwoo. "Investigating and Improving Bridge Management System Methodologies Under Uncertainty." DigitalCommons@USU, 2016. https://digitalcommons.usu.edu/etd/5039.
Full textAbstract:
This dissertation presents a novel procedure to select explanatory variables, without the influence of human bias, for deterioration model development using National Bridge Inventory (NBI) data. Using NBI information, including geometric data and climate information, candidate explanatory variables can be converted into normalized numeric values and analyzed prior to the development of deterministic or stochastic deterioration models. The prevailing approach for explanatory variable selection is to use expert opinion solicited from experienced engineers. This may introduce human influenced biases into the deterioration modeling process. A framework using Least Absolute Shrinkage and Selection Operator (LASSO) penalized regression and covariance analysis are combined to compensate for this potential bias. Additionally, the cross validation analysis and solution path is used as a standard for the selection of minimum number of explanatory variables.
The proposed method is demonstrated through the creation of deterministic deterioration models for deck, superstructure, and substructure for Wyoming bridges and compared to explanatory variables using the expert selection method. The comparison shows a significant decrease in error using the presented framework based on the L2 relative error norm.
The final chapter presents a new method to develop stochastic deterioration models using logistic regression. The relative importance amongst explanatory variables is used to develop a classification tree for Wyoming bridges. The bridges in a subset are commonly associated with several explanatory variables, so that the deterioration models can be more representative and accurate than using a single explanatory variable. The logistic regression is used to introduce the stochastic contribution into the deterioration models. In order to avoid missing data problems, the binary categories condition rating, either remaining the same or decreased, are considered for logistic regression. The probability of changes in bridges’ condition rating is obtained and the averages for same condition ratings are used to create transition probability matrix for each age group.
The deterioration model based on Markov chain are developed for Wyoming bridges and compared with the previous model based on percentage prediction and optimization approach. The prediction error is analyzed, which demonstrates the considerable performance of the proposed method and is suitable for relatively small data samples.
13
Durmus, Beste Gul. "Long-term multi-objective project selection under uncertainty." FIU Digital Commons, 2007. http://digitalcommons.fiu.edu/etd/3108.
Full text
14
Van, de Putte Alexander. "Rational valuation under uncertainty : a real options approach." Thesis, Birkbeck (University of London), 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248031.
Full text
15
Manikas, Andrew Steven. "Essays in inventory decisions under uncertainty." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/22647.
Full textAbstract:
Thesis (Ph. D.)--Management, Georgia Institute of Technology, 2008.Committee Chair: Yih-Long Chang; Committee Member: Paul Griffin; Committee Member: Ravi Subramanian; Committee Member: Soumen Ghosh; Committee Member: Srinagesh Gavirneni.
16
Qi, Honghai. "Integrated watershed management and agricultural land-use optimization under uncertainty /." Full text available from ProQuest UM Digital Dissertations, 2006. http://0-proquest.umi.com.umiss.lib.olemiss.edu/pqdweb?index=0&did=1800276551&SrchMode=1&sid=13&Fmt=2&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1258492716&clientId=22256.
Full textAbstract:
Thesis (Ph.D.)--University of Mississippi, 2006.Typescript. Vita. Major professor: Dr. Mustafa Altinakar "December 2006." Includes bibliographical references (leaves 217-239). Also available online via ProQuest to authorized users.
17
Forsell, Nicklas. "Planning under risk and uncertainty : optimizing spatial forest management strategies /." Umeå : Dept. of Forest Resource Management, Swedish University of Agricultural Sciences, 2009. http://epsilon.slu.se/200939.pdf.
Full text
18
Roach, Thomas Peter. "Decision making methods for water resources management under deep uncertainty." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/25756.
Full textAbstract:
Substantial anthropogenic change of the Earth’s climate is modifying patterns of rainfall, river flow, glacial melt and groundwater recharge rates across the planet, undermining many of the stationarity assumptions upon which water resources infrastructure has been historically managed. This hydrological uncertainty is creating a potentially vast range of possible futures that could threaten the dependability of vital regional water supplies. This, combined with increased urbanisation and rapidly growing regional populations, is putting pressures on finite water resources. One of the greatest international challenges facing decision makers in the water industry is the increasing influences of these “deep” climate change and population growth uncertainties affecting the long-term balance of supply and demand and necessitating the need for adaptive action. Water companies and utilities worldwide are now under pressure to modernise their management frameworks and approaches to decision making in order to identify more sustainable and cost-effective water management adaptations that are reliable in the face of uncertainty. The aim of this thesis is to compare and contrast a range of existing Decision Making Methods (DMMs) for possible application to Water Resources Management (WRM) problems, critically analyse on real-life case studies their suitability for handling uncertainties relating to climate change and population growth and then use the knowledge generated this way to develop a new, resilience-based WRM planning methodology. This involves a critical evaluation of the advantages and disadvantages of a range of methods and metrics developed to improve on current engineering practice, to ultimately compile a list of suitable recommendations for a future framework for WRM adaptation planning under deep uncertainty. This thesis contributes to the growing vital research and literature in this area in several distinct ways. Firstly, it qualitatively reviews a range of DMMs for potential application to WRM adaptation problems using a set of developed criteria. Secondly, it quantitatively assesses two promising and contrasting DMMs on two suitable real-world case studies to compare highlighted aspects derived from the qualitative review and evaluate the adaptation outputs on a practical engineering level. Thirdly, it develops and reviews a range of new potential performance metrics that could be used to quantitatively define system resilience to help answer the water industries question of how best to build in more resilience in future water resource adaptation planning. This leads to the creation and testing of a novel resilience driven methodology for optimal water resource planning, combining optimal aspects derived from the quantitative case study work with the optimal metric derived from the resilience metric investigation. Ultimately, based on the results obtained, a list of suitable recommendations is compiled on how to improve the existing methodologies for future WRM planning under deep uncertainty. These recommendations include the incorporation of more complex simulation models into the planning process, utilisation of multi-objective optimisation algorithms, improved uncertainty characterisation and assessments, an explicit robustness examination and the incorporation of additional performance metrics to increase the clarity of the strategy assessment process.
19
Kamgaing, Moyo Clinsort. "Optimal hedging under price, quantity and exchange rate uncertainty." Thesis, Massachusetts Institute of Technology, 1986. http://hdl.handle.net/1721.1/37696.
Full textAbstract:
Thesis (M.S.)--Massachusetts Institute of Technology, Sloan School of Management, 1986.MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY
Bibliography: leaf 46.
by Moyo Clinsort Kamgaing.
M.S.
20
Luo, Sifo. "Capacity planning under demand and manufacturing uncertainty for biologics." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112865.
Full textAbstract:
Thesis: M. Eng. in Supply Chain Management, Massachusetts Institute of Technology, Supply Chain Management Program, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (page 58).
Due to the long lead times and complexity in drug development and approval processes, pharmaceutical companies use long range planning to plan their production for the next 10 years. Capacity planning is largely driven by the long-term demand and its forecast uncertainty. The impact of uncertainties at manufacturing level, such as factory productivity and production success rate, are not entirely taken into account since only the average values of each manufacturing parameter are used. Can we better allocate production among manufacturing facilities when both demand and manufacturing uncertainties are considered? In this thesis a stochastic optimization approach is followed to minimize the deviation from target capacity limit under different manufacturing and demand scenarios. The mixed integer linear model incorporates the impact of demand and manufacturing variation on production allocation among manufacturing facilities through Monte Carlo generated scenarios. The thesis model is designed in a way that can be used as a decision tool to perform robust capacity planning at the strategic level.
by Sifo Luo.
M. Eng. in Supply Chain Management
21
Dörr, Ulrike [Verfasser]. "Subjective Self-Assessment and Decision Making under Uncertainty / Ulrike Dörr." Kiel : Universitätsbibliothek Kiel, 2013. http://d-nb.info/1036406296/34.
Full text
22
Krumholz, Dr Hans-Christian [Verfasser]. "Essays on Corporate Valuation under Uncertainty / Dr. Hans-Christian Krumholz." München : GRIN Verlag, 2016. http://d-nb.info/1184108382/34.
Full text
23
Suwandechochai, Rawee. "Analysis of Decision Postponement Strategies for Aircraft Assignment under Uncertainty." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/31863.
Full textAbstract:
The ability to effectively match supply and demand can lead to significant revenue benefits in the airline industry. Airline supply management deals with assigning the right resources (i.e., aircraft and crew) to the right routes in the flight network. Due to certain crew regulations, operating characteristics, and constraints of the airline companies, these supply management decisions need to be made well in advance of departures, at a time when demand is highly uncertain. However, demand forecasts improve markedly over time, as more information on demand patterns is gathered. Thus, exploiting the flexibilities in the system that allows the partial postponement of supply decisions to a later time, when more accurate demand information is obtained, can significantly improve the airline's revenue.
In this thesis, we propose and analyze the Demand Driven Swapping (DDS) approach that aims at improving the airline's revenue by reducing the supply-demand mismatches through dynamically swapping aircraft as departures approach. This research has been done in collaboration with our industrial partner, the United Airlines Research and Development Division.
Due to the proximity to departures, the DDS problem is restricted by two main constraints:
1) the initial crew schedule needs to be kept intact (due to certain union contracts); and
2) airport services and operations need to be preserved to the greatest extent possible.
As a result, only a limited number of simple swaps can be performed between aircraft types of the same family (i.e. crew-compatible aircraft types). However, the swaps can be potentially performed on a daily basis given the initial fleet assignments. Clearly, the swapping criteria, frequency, and timing will highly impact the revenue benefits of the DDS approach. When the swapping decisions are made several weeks prior to departures (i.e., 4-6 weeks before departures), they will not cause much disturbance to the operations, but will be performed under highly uncertain demand information. On the other hand, swapping decisions that are delayed to a time later (i.e., 1-3 weeks before departures) will decrease the possibility of bad swaps, but will result in larger costs due to the higher disruptions to airport services and operations.
Thus our research objective is to provide guidelines and principles on how the flexible capacity should be managed in the system. For this purpose, we study the effectiveness of different swapping strategies, characterized in terms of their frequency and timing, for hedging against the demand uncertainty.
We first study stylized analytical models to gain insights into the critical parameters that affect these benefits. Simulation models are then conducted to test the validity of our analytical findings as well as to analyze more complex strategies and assess the dynamic performance of these strategies.
The analytical results indicate that strategies that make the swapping decision early in time (in order to minimize disturbances to the operations) perform very well on routes, where the demand uncertainty is low and the expected demands on the legs are well-balanced. Otherwise, a swapping strategy, which revises the swapping decision over time, should be implemented. Our simulation results, based on real data obtained from United Airlines, confirm the analytical findings.Master of Science
24
Radke, Naomi [Verfasser], Marc [Akademischer Betreuer] Hanewinkel, and Klaus [Akademischer Betreuer] Keller. "Robust decision making for forest management under climate change and uncertainty." Freiburg : Universität, 2020. http://d-nb.info/1226656943/34.
Full text
25
BRUNO, SERGIO VITOR DE BARROS. "STRATEGIC RISK MANAGEMENT: A FRAMEWORK FOR RENEWABLE GENERATION INVESTMENT UNDER UNCERTAINTY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=27453@1.
Full textAbstract:
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIORPROGRAMA DE EXCELENCIA ACADEMICA
O investimento em fontes renováveis, apesar do crescimento recente, ainda é dificultado devido à volatilidade dos mercados de curto prazo. Contratos forward são essenciais mesmo em mercados de balcão como o Ambiente de Contratação Livre (ACL) Brasileiro. Contatos forward permitem a redução da incerteza sobre a receita, ajudam a garantir a adequação do fornecimento graças à sinalização de preços para a expansão e podem também ser obrigatórios para realização do project finance de novos empreendimentos. Apesar da oferta de contratos, as fontes renováveis ainda possuem o risco adicional em sua geração, o que pode, combinando-se altos preços spot em um momento de baixa geração, ocasionar uma exposição ao risco de preço-quantidade. Investimento em fontes renováveis pode ser incentivado através da aplicação de técnicas de gestão de riscos como contratação forward, diversificação e definição do momento ótimo de investimento. Através da negociação de contratos e aproveitando complementariedades sazonais entre as fontes, é possível minimizar a exposição aos riscos do mercado. O problema de investimento em centrais de energia renovável pode ser visto como um modelo de otimização estocástica multiestágio com variáveis inteiras, de difícil resolução. As principais soluções disponíveis na literatura simplificam o problema ao reduzir a dimensionalidade da árvore de cenários, ou assumindo hipóteses simplificadoras sobre os processos estocásticos. Nosso objetivo é apresentar um framework para valoração de investimentos em energia renovável, considerando as principais fontes de incerteza e alternativas para composição de uma carteira de investimentos. A principal contribuição desse trabalho é uma metodologia para resolver, utilizando técnicas de decomposição, o problema de investimento ótimo em centrais renováveis complementares no mercado elétrico brasileiro. Este é um problema estocástico multiestágio e não convexo. Nossas políticas de investimento são geradas através de um algoritmo baseado em Programação Dinâmica Dual Estocástica (SDDP). Restrições de integralidade são consideradas no passo forward, onde as políticas são avaliadas, e relaxados no passo backward, onde as políticas são geradas, para garantir a convexidade das funções de recurso. Os resultados numéricos mostram que não é possível assumir independência entre estágios dos processos estocásticos de preços. A estrutura Markoviana dos processos estocásticos é preservada usando uma discretização do espaço de probabilidade, que é resolvida utilizando uma conhecida extensão do SDDP. A avaliação da performance é feita utilizando os dados originais, validando nossa heurística. Nosso framework requer um modelo para o preço forward de energia. Nós aplicamos o modelo Schwartz-Smith usando dados do mercado spot e de balcão para construir a curva forward do mercado brasileiro. O framework contempla as particularidades do ACL no mercado brasileiro, mas também pode ser utilizado em mercados similares. Utilizando medidas coerentes de risco, incorporamos aversão a risco e avaliamos as estratégias concorrentes utilizando conceitos modernos de gestão de riscos.
Despite recent trend for investment in renewable energy, high volatility in shortterm markets still may hinder some opportunities. Forwarding contracting is essential even in Over The Counter (OTC) markets such as the Brazilian Free Trading Environment. Forward contracts allow reducing revenue uncertainty, help ensure supply adequacy by signaling generation expansion and may also be required for project financing in new ventures. Still, renewable sources face the additional risk of uncertain generation, which, in low periods, combined with high spot prices, pose the hazardous price-quantity risk. Renewable investment may be fostered by applying risk management techniques such as forward contracting, diversification and optimal investment timing. By trading contracts and exploiting the seasonal complementarity of the renewable sources, it is possible to reduce risk exposure. The problem of investment in renewable energy plants may be seen as a multistage stochastic optimization model with integer variables, which is very hard to solve. The main approaches in the current literature simplify the problem by reducing the dimensionality of the scenario tree or by assuming simplifying hypothesis on the stochastic processes. Our objective is to introduce a renewable investment valuation framework, considering the main uncertainty sources and portfolio investment alternatives. The main contribution of this work is a method to solve, by applying decomposition techniques, the problem of optimal investment in seasonal complementary renewable plants in the Brazilian energy market. This is a multistage stochastic and non-convex problem. Our investment policies are devised using an algorithm based on Stochastic Dual Dynamic Programming (SDDP). Integrality constraints are considered in the forward step, where policies are evaluated, and relaxed in the backward step, where policies are built, to ensure convexity of the recourse functions. Numerical results show that it is not possible to assume stagewise independence of the price processes. We maintain the Markovian property of the stochastic processes by a discretization of the probability space, solvable by a known extension to the SDDP method. Performance evaluation is carried out using the original data, validating our heuristic. A forward energy price model is required in our framework. We apply the Schwartz-Smith model with spot and OTC data of the Brazilian market to build such a forward price curve. The framework is able to represent the characteristics of the Brazilian FTE and may be applied to similar markets. We incorporate risk aversion with coherent measures of risk and evaluate alternative strategies based on modern risk management concepts.
26
Xu, Zhiyu 1973. "Two approaches to buffer management under demand uncertainty : an analytical process." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28520.
Full textAbstract:
Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2004.Includes bibliographical references (leaves 66-67).
(cont.) boundary and leave more demand uncertainty to the pull part of the system.
Based on a particular case study, this paper presents two approaches to buffer management under demand uncertainty, which is characterized by high lumpiness, dispersion and volatility. The common theme of both of the two approaches is not to find an advanced statistical method to improve demand forecast on the basis of historical data. Rather, these approaches provide new business paradigms to deal with demand uncertainty. The first approach, make-to-anticipated-order (MTAO), takes advantage of the mechanism of make-to-order (MTO) and develops a process that the production is pulled by anticipated orders instead of being pushed by the forecast of unpredictable future demand. The implementation of this method, on one hand, breaks through the precondition of MTO that the total production cycle time should be less than customers' desired lead-time. On the other hand, MTAO enjoys the advantage of arranging production by responding to customer demand to reduce inventory costs and obsolescence risks of MPS level items. The second approach makes use of postponement and commonality strategy to lower demand uncertainty. The basic principle is that aggregate demand is more stable than disaggregate demand. Thus, if a common module instead of various individual modules in a module family acts as a MPS item, the demand of the common module will represent the aggregate demand of all individual modules in the module family and more accurate forecast can be made. Then by using the forecasted demand distribution of the common module, we can figure out optimized multistage inventory placement to buffer demand uncertainty with the minimum holding cost of total safety stock. In effect, by implementing postponement and commonality strategy, we change the push-pull
by Zhiyu Xu.
M.Eng.in Logistics
27
Müller, Jan [Verfasser], and H. [Akademischer Betreuer] Schmeck. "Optimization Under Uncertainty in Building Energy Management / Jan Müller ; Betreuer: H. Schmeck." Karlsruhe : KIT-Bibliothek, 2019. http://d-nb.info/1196208654/34.
Full text
28
Zhou, Yi. "Simulation and Performance Analysis of Strategic Air Traffic Management under Weather Uncertainty." Thesis, University of North Texas, 2011. https://digital.library.unt.edu/ark:/67531/metadc68071/.
Full textAbstract:
In this thesis, I introduce a promising framework for representing an air traffic flow (stream) and flow-management action operating under weather uncertainty. I propose to use a meshed queuing and Markov-chain model---specifically, a queuing model whose service-rates are modulated by an underlying Markov chain describing weather-impact evolution---to capture traffic management in an uncertain environment. Two techniques for characterizing flow-management performance using the model are developed, namely 1) a master-Markov-chain representation technique that yields accurate results but at relatively high computational cost, and 2) a jump-linear system-based approximation that has promising scalability. The model formulation and two analysis techniques are illustrated with numerous examples. Based on this initial study, I believe that the interfaced weather-impact and traffic-flow model analyzed here holds promise to inform strategic flow contingency management in NextGen.
29
Yoo, Woosang. "Valuation of metal reserves under price uncertainty by using option pricing model." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10979.
Full text
30
Shi, Yuan, and 石园. "A portfolio approach to procurement planning and risk hedging under uncertainty." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44905051.
Full text
31
Basupi, Innocent. "Adaptive water distribution system design under future uncertainty." Thesis, University of Exeter, 2013. http://hdl.handle.net/10871/14722.
Full textAbstract:
A water distribution system (WDS) design deals with achieving the desired network performance. WDS design can involve new and / or existing network redesigns in order to keep up with the required service performance. Very often, WDS design is expensive, which encourages cost effectiveness in the required investments. Moreover, WDS design is associated with adverse environmental implications such as greenhouse gas (GHG) emissions due to energy consumption. GHGs are associated with global warming and climate change. Climate change is generally understood to cause reduction in water available at the sources and increase water demand. Urbanization that takes into account factors such as demographics (population ageing, household occupancy rates, etc.) and other activities are associated with water demand changes. In addition to the aforementioned issues, the challenge of meeting the required hydraulic performance of WDSs is worsened by the uncertainties that are associated with WDS parameters (e.g., future water demand). With all the factors mentioned here, mitigation and adaptive measures are considered essential to improve WDS performance in the long-term planning horizon. In this thesis, different formulations of a WDS design methodologies aimed at mitigating or adapting the systems to the effects of future changes such as those of climate change and urbanization are explored. Cost effective WDS designs that mitigate climate change by reducing GHG emissions have been investigated. Also, water demand management (DM) intervention measures, i.e., domestic rainwater harvesting (RWH) systems and water saving appliance schemes (WSASs) have been incorporated in the design of WDSs in an attempt to mitigate, adapt to or counteract the likely effects of future climate change and urbanization. Furthermore, flexibility has been introduced in the long-term WDS design under future uncertainty. The flexible methodology is adaptable to uncertain WDS parameters (i.e., future water demand in this thesis) thereby improving the WDS economic cost and hydraulic performance (resilience). The methodology is also complimented by strategically incorporating DM measures to further enhance the WDS performance under water demand uncertainty. The new methodologies presented in this thesis were successfully tested on case studies. Finally, conclusions and recommendations for possible further research work are made. There are potential benefits (e.g., cost savings, additional resilience, and lower GHG emissions) of incorporating an environmental objective and DM interventions in WDS design. Flexibility and DM interventions add value in the design of WDSs under uncertainty.
32
Xu, Guanglin. "Optimization under uncertainty: conic programming representations, relaxations, and approximations." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5881.
Full textAbstract:
In practice, the presence of uncertain parameters in optimization problems introduces new challenges in modeling and solvability to operations research. There are three main paradigms proposed for optimization problems under uncertainty. These include stochastic programming, robust optimization, and sensitivity analysis. In this thesis, we examine, improve, and combine the latter two paradigms in several relevant models and applications. In the second chapter, we study a two-stage adjustable robust linear optimization problem in which the right-hand sides are uncertain and belong to a compact, convex, and tractable uncertainty set. Under standard and simple assumptions, we reformulate the two-stage problem as a copositive optimization program, which in turns leads to a class of tractable semidefinite-based approximations that are at least as strong as the affine policy, which is a well studied tractable approximation in the literature. We examine our approach over several examples from the literature and the results demonstrate that our tractable approximations significantly improve the affine policy. In particular, our approach recovers the optimal values of a class of instances of increasing size for which the affine policy admits an arbitrary large gap. In the third chapter, we leverage the concept of robust optimization to conduct sensitivity analysis of the optimal value of linear programming (LP). In particular, we propose a framework for sensitivity analysis of LP problems, allowing for simultaneous perturbations in the objective coefficients and right-hand sides, where the perturbations are modeled in a compact, convex, and tractable uncertainty set. This framework unifies and extends multiple approaches for LP sensitivity analysis in the literature and has close ties to worst-case LP and two-stage adjustable linear programming. We define the best-case and worst-case LP optimal values over the uncertainty set. As the concept aligns well with the general spirit of robust optimization, we denote our approach as robust sensitivity analysis. While the best-case and worst-case optimal values are difficult to compute in general, we prove that they equal the optimal values of two separate, but related, copositive programs. We then develop tight, tractable conic relaxations to provide bounds on the best-case and worst case optimal values, respectively. We also develop techniques to assess the quality of the bounds, and we validate our approach computationally on several examples from—and inspired by—the literature. We find that the bounds are very strong in practice and, in particular, are at least as strong as known results for specific cases from the literature. In the fourth chapter of this thesis, we study the expected optimal value of a mixed 0-1 programming problem with uncertain objective coefficients following a joint distribution. We assume that the true distribution is not known exactly, but a set of independent samples can be observed. Using the Wasserstein metric, we construct an ambiguity set centered at the empirical distribution from the observed samples and containing all distributions that could have generated the observed samples with a high confidence. The problem of interest is to investigate the bound on the expected optimal value over the Wasserstein ambiguity set. Under standard assumptions, we reformulate the problem into a copositive programming problem, which naturally leads to a tractable semidefinite-based approximation. We compare our approach with a moment-based approach from the literature for two applications. The numerical results illustrate the effectiveness of our approach.
Finally, we conclude the thesis with remarks on some interesting open questions in the field of optimization under uncertainty. In particular, we point out that some interesting topics that can be potentially studied by copositive programming techniques.
33
Chang, Yu-Heng. "Stochastic programming approaches to air traffic flow management under the uncertainty of weather." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37183.
Full textAbstract:
As air traffic congestion grows, air traffic flow management (ATFM) is becoming a great concern. ATFM deals with air traffic and the efficient utilization of the airport and airspace. Air traffic efficiency is heavily influenced by unanticipated factors, or uncertainties, which can come from several sources such as mechanical breakdown; however, weather is the main unavoidable cause of uncertainty. Because weather is unpredictable, it poses a critical challenge for ATFM in current airport and airspace operations. Convective weather results in congestion at airports as well as in airspace sectors. During times of congestion, the decision as how and when to send aircraft toward an airspace sector in the presence of weather is difficult. To approach this problem, we first propose a two-stage stochastic integer program by emphasizing a given single sector. By considering ground delay, cancellation, and cruise speed for each flight on the ground in the first stage, as well as air holding and diversion recourse actions for each flight in the air in the second stage, our model determines how aircraft are sent toward a sector under the uncertainty of weather. However, due to the large number of weather scenarios, the model is intractable in practice. To overcome the intractability, we suggest a rolling horizon method to solve the problem to near optimal. Lagrangian relaxation and subgradient method are used to justify the rolling horizon method. Since the rolling horizon method can be solved in real time, we can apply it to actual aircraft schedules to reduce the costs incurred on the ground as well as in airspace. We then extend our two-stage model to a multistage stochastic program, which increases the number of possible weather realizations and results a more efficient schedule in terms of costs. The rolling horizon method as well as Lagrangian relaxation and subgradient method are applied to this multistage model. An overall comparison among the previously described methodologies are presented.
34
Tinh, Pham Van. "A simulation-based multi-criteria management system for optimal water supply under uncertainty." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-165776.
Full textAbstract:
For cost and reliability efficiency, optimal design and operation of pressurized water distribution networks is highly important. However, optimizing such networks is still a challenge since it requires an appropriate determination of: (1) dimension of pipe / pump / tank - decision variables (2) cost / network reliability - objective functions and (3) limits or restrictions within which the network must operate - a given set of constraints. The costs mentioned here consist in general of capital, construction, and operation costs. The reliability of a network mainly refers to the intrinsic capability of providing water with adequate volume and a certain pressure to consumers under normal and extreme conditions. These contradicting objective functions are functions of network configuration regarding component sizes and network layout. Because considerable uncertainties finally render the overall task to a highly complex problem, most recent approaches mainly focus only on finding a trade-off between minimizing cost and maximizing network reliability. To overcome these limitations, a novel model system that simultaneously considers network configuration, its operation and the relevant uncertainties is proposed in this study.
For solving this multi-objective design problem, a simulation-based optimization approach has been developed and applied. The approach couples a hydraulic model (Epanet) with the covariance matrix adaptation evolution strategy (CMA-ES) and can be operated in two different modes. These modes are (1) simulation–based Single-objective optimization and (2) simulation-based multi-objective optimization. Single-objective optimization yields the single best solution with respect to cost or network reliability, whereas multi-objective optimization produces a set of non-dominated solutions called Pareto optimal solutions which are trade-offs between cost and reliability.
In addition, to prevent a seriously under-designed network, demand uncertainties was also taken into account through a so called “robustness probability” of the network. This consideration may become useful for a more reliable water distribution network.
In order to verify the performance of the proposed approach, it was systematically tested on a number of different benchmark water distribution networks ranging from simple to complex. These benchmark networks are either gravity-fed or pumped networks which need to be optimally designed to supply urban or irrigation water demand under specific constraints. The results show that the new approach is able:
• to solve optimization problems of pressurized water distribution network design and operation regarding cost and network reliability;
• to directly determine the pumping discharge and head, thus allowing to select pumps more adequately;
• to simulate time series of tank water level;
• to eliminate redundant pipes and pumps to generate an optimal network layout;
• to respond well to complex networks other than only to simple networks;
• to perform with multiple demand loading;
• to produce reliable Pareto optimal solutions regarding multi-objective optimization.
In conclusion, the new technique can be successfully applied for optimization problems in pressurized water distribution network design and operation. The new approach has been demonstrated to be a powerful tool for optimal network design not only for irrigation but also for an urban water supply.
35
Hou, Chengjun. "Dynamic Programming under Parametric Uncertainty with Applications in Cyber Security and Project Management." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1437676379.
Full text
36
Haro, Monteagudo David. "Methodology for the optimal management design of water resources system under hydrologic uncertainty." Doctoral thesis, Universitat Politècnica de València, 2015. http://hdl.handle.net/10251/45996.
Full textAbstract:
Un sistema de gestión de sequías apropiado requiere de la anticipación de los posibles
efectos que un episodio de este tipo tenga sobre el sistema de recursos hídricos. Esta tarea sin
embargo resulta más complicada de lo que parece. En primer lugar, debido al alto grado de
incertidumbre existente en la predicción de variables hidrológicas futuras. Y en segundo,
debido al riesgo de sobrerreacción en la activación de medidas de mitigación generando falsa
sensación de escasez, o sequía artificial. A este respecto, los planes especiales de sequía
proveen de herramientas para la gestión eficiente de situaciones con escasez de recursos y la
preparación de cara a futuros eventos. De todos modos, las diferentes estrategias de
operación seguidas en cada sistema de recursos hídricos hacen que las herramientas que en
algunos casos resultaron altamente útiles no lo sean tanto cuando se aplican en sistemas
distintos.
Debido a la falta de tiempo y/o al exceso de confianza en los trabajos realizados por
terceros, con excelentes resultados en sus respectivos casos, a veces se cae en el error de
implementar metodologías no del todo apropiadas en sistemas con requisitos completamente
distintos. El desarrollo y utilización de metodologías generalizadas aplicables a diferentes
sistemas y capaces de proporcionar resultados adaptados a cada caso es, por tanto, muy
deseable. Este es el caso de las herramientas de modelación de sistemas de recursos hídricos
generalizadas. Estas permiten homogeneizar los procesos mientras siguen siendo los
suficientemente adaptables para proporcionar resultados apropiados para cada caso de
estudio.
Esta tesis presenta una serie de herramientas destinadas a avanzar en el análisis y
comprensión de los sistemas de recursos hídricos, haciendo énfasis en la prevención de
sequías y la gestión de riesgos. Las herramientas desarrolladas incluyen: un modelo de
optimización generalizado para esquemas de recursos hídricos, con capacidad para la
representación detallada de cualquier sistema de recursos hídricos, y una metodología de
análisis de riesgo basada en la optimización de Monte Carlo con múltiples series sintéticas. Con
estas herramientas es posible incluir tanto la componente superficial como la subterránea del
sistema estudiado dentro del proceso de optimización. La optimización está basada en la
resolución iterativa de redes de flujo. Se probó la consistencia y eficiencia de diferentes
algoritmos de resolución para encontrar un balance entre la velocidad de cálculo, el número
de iteraciones, y la consistencia de los resultados, aportando recomendaciones para el uso de
cada algoritmo dadas las diferencias entre los mismos.
Las herramientas desarrolladas se aplican en dos casos de estudio reales en la
evaluación y posibilidad de complementación de los sistemas de monitorización y alerta
temprana de sequías existentes en los mismos. En el primer caso, se propone un enfoque
alternativo para la monitorización de la sequía en el sistema de operación anual del río Órbigo
(España), complementándolo con la utilización de la metodología de análisis de riesgo. En el
segundo caso, las herramientas se emplean en un sistema con una estrategia de operación
completamente distinta. Se estudia como el análisis de riesgo de la gestión óptima puede
ayudar a la activación anticipada de los escenarios de sequía en los sistemas de los ríos Júcar y
Turia, cuya operación es hiperanual. En esta ocasión, el sistema de indicadores existente goza
de una gran confianza por parte de los usuarios. La metodología de análisis de riesgo es, sin
embargo, capaz de anticipar los eventos de sequía con mayor alarma, aspecto que es deseable
si se quiere evitar que los episodios en desarrollo vayan a más. En ambos casos se muestra
como la evaluación anticipada de las posibles situaciones futuras del sistema permiten una
definición confiable de los escenarios de sequía con suficiente antelación para la activación
efectiva de medidas de prevención y/o mitigación en caso de ser necesarias.
La utilización de indicadores provenientes de modelos frente a indicadores basados en
datos observados es complementaria y ambos deberían utilizarse de forma conjunta para
mejorar la gestión preventiva de los sistemas de recursos hídricos. El empleo de modelos de
optimización en situaciones de incertidumbre hidrológica es muy apropiado gracias a la no
necesidad de definir reglas de gestión para obtener los mejores resultados del sistema, y
teniendo en cuenta que las reglas de operación habituales pueden no ser completamente
adecuadas en estas ocasiones.Haro Monteagudo, D. (2014). Methodology for the optimal management design of water resources system under hydrologic uncertainty [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/45996
TESIS
37
Volk, Rebekka [Verfasser]. "Proactive-reactive, robust scheduling and capacity planning of deconstruction projects under uncertainty / Rebekka Volk." Karlsruhe : KIT Scientific Publishing, 2017. http://www.ksp.kit.edu.
Full text
38
Page, Alison L. 1971. "Forecasting mix-sensitive semiconductor fabrication tool set requirements under demand uncertainty." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/84517.
Full textAbstract:
Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2001.Includes bibliographical references (leaves 74-75).
by Alison L. Page.
S.M.
M.B.A.
39
Höllermann, Britta [Verfasser]. "Decision-making under uncertainty in model-based water management : The science-practice interface / Britta Höllermann." Bonn : Universitäts- und Landesbibliothek Bonn, 2018. http://d-nb.info/1167857070/34.
Full text
40
Seo, Sangtaek. "Effects of federal risk management programs on investment, production, and contract design under uncertainty." Texas A&M University, 2004. http://hdl.handle.net/1969.1/3117.
Full textAbstract:
Agricultural producers face uncertain agricultural production and market
conditions. Much of the uncertainty faced by agricultural producers cannot be controlled
by the producer, but can be managed. Several risk management programs are available
in the U.S. to help manage uncertainties in agricultural production, marketing, and
finance. This study focuses on the farm level economic implications of the federal risk
management programs. In particular, the effects of the federal risk management
programs on investment, production, and contract design are investigated.
The dissertation is comprised of three essays. The unifying theme of these
essays is the economic analysis of crop insurance programs. The first essay examines
the effects of revenue insurance on the entry and exit thresholds of table grape producers
using a real option approach. The results show that revenue insurance decreases the
entry and exit thresholds compared with no revenue insurance, thus increasing the
investment and current farming operation. If the policy goal is to induce more farmers
in grape farming, the insurance policy with a high coverage level and high subsidy rate
is effective.
In the second essay, a mathematical programming model is used to examine the
effects of federal risk management programs on optimal nitrogen fertilizer use and land
allocation simultaneously. Current insurance programs and the Marketing Loan
Program increase the optimal fertilizer rate 2% and increase the optimal cotton acreage
119-130% in a Texas cotton-sorghum system. Assuming nitrogen is harmful to the
environment and cotton requires higher nitrogen use, these risk management programs
counteract federal environmental programs.
The third essay uses a principal-agent model to examine the optimal contract
design that induces the best effort from the farmer when crop insurance is purchased.
With the introduction of crop insurance, the investorÂs optimal equity financing contract
requires that the farmer bear more risk in order to have the incentive to work hard, which
is achieved by increasing variable compensation and decreasing fixed compensation.
41
Kaplan, Pervin Ozge. "Consideration of Cost and Environmental Emissions of Solid Waste Management under Conditions of Uncertainty." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20011129-105110.
Full textAbstract:
Among the many models and tools available for solid waste management (SWM), the integrated SWM decision support tool (ISWM DST) developed at North Carolina State University provides a comprehensive and integrated approach that considers cost and environmental factors associated with a large set of waste processing options. ISWM DST is designed to generate alternative SWM strategies that meet user-defined cost and environmental objectives. In addition to an array of site-specific inputs, this tool includes a large number of model parameters, which are currently treated deterministically with point estimates for inputs. A high degree of variability and uncertainty is known to exist in these input parameters, affecting the uncertainty in the model outputs. The absence of a systematic procedure to consider uncertainty in ISWM DST is a major drawback. The goal of this study is to develop and incorporate an uncertainty analysis component into ISWM DST. A Latin Hypercube Sampling (LHS) procedure is coupled with a simulation approach to enable uncertainty propagation. The capabilities of this new component are demonstrated using a realistic case study in which a series of scenarios are examined assuming uncertainty in a subset of the input parameters. For each scenario, the alternative strategy development capabilities of ISWM DST is first applied, the then each SWM strategy is evaluated under conditions of uncertainty. Performance of alternative strategies is compared, and more reliable or robust strategies are identified. New and useful insights that were not apparent under deterministic conditions were gained, contributing more information to assist in SWM decision making. Further, correlation analysis was conducted to identify the uncertain input parameters that contribute mostly to the output uncertainty. This information is also expected to be valuable in making more informed decisions. In summary, this research contributes by significantly enhancing via the uncertainty analysis component the broad array of powerful capabilities of ISWM DST, making this tool more applicable in SWM planning and design practice.
42
Bartl, Daniel. "The strategic management of coherence : how to keep a firm on track under uncertainty /." [S.l. : s.n.], 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017049650&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
Full text
43
Sheikh, Hussin Siti Aida. "Employees Provident Fund (EPF) Malaysia : generic models for asset and liability management under uncertainty." Thesis, Brunel University, 2012. http://bura.brunel.ac.uk/handle/2438/7505.
Full textAbstract:
We describe Employees Provident Funds (EPF) Malaysia. We explain about Defined Contribution and Defined Benefit Pension Funds and examine their similarities and differences. We also briefly discuss and compare EPF schemes in four Commonwealth countries. A family of Stochastic Programming Models is developed for the Employees Provident Fund Malaysia. This is a family of ex-ante decision models whose main aim is to manage, that is, balance assets and liabilities. The decision models comprise Expected Value Linear Programming, Two Stage Stochastic Programming with recourse, Chance Constrained Programming and Integrated Chance Constraints Programming. For the last three decision models we use scenario generators which capture the uncertainties of asset returns, salary contributions and lump sum liabilities payments. These scenario generation models for Assets and liabilities were developed and calibrated using historical data. The resulting decisions are evaluated with in-sample analysis using typical risk adjusted performance measures. Out- of- sample testing is also carried out with a larger set of generated scenarios. The benefits of two stage stochastic programming over deterministic approaches on asset allocation as well as the amount of borrowing needed for each pre-specified growth dividend are demonstrated. The contributions of this thesis are i) an insightful overview of EPF ii) construction of scenarios for assets returns and liabilities with different values of growth dividend, that combine the Markov population model with the salary growth model and retirement payments iii) construction and analysis of generic ex-ante decision models taking into consideration uncertain asset returns and uncertain liabilities iv) testing and performance evaluation of these decisions in an ex-post setting.
44
Kuhlmann, Salas Claudio Andrés. "Ellipsoidal forest and wildland fire scar scenarios for strategic forest management planning under uncertainty." Tesis, Universidad de Chile, 2014. http://repositorio.uchile.cl/handle/2250/131350.
Full textAbstract:
Magister en Gestión de OperacionesIngeniero Civil Industrial
La importancia que ha tomado la conservación del medioambiente ha ido en aumento, lo que ha afectado directamente en los objetivos y forma de operar de las organizaciones. Es por esto que la interacción entre la operación y el desarrollo del ecosistema debe ser considerada para balancear la sustentabilidad y conservación con los objetivos productivos, siendo las perturbaciones forestales un punto de gran interés. Incendios, plagas, erupciones volcánicas e inundaciones son algunas de las perturbaciones al ecosistema que afectan la productividad del bosque. Por lo tanto, reducir el riesgo y las consecuencias de estos episodios es clave para la industria. El objetivo es crear una metodología que permita generar escenarios de incendios elipsoidales para su utilización en la toma de decisiones en el manejo de incendios y recursos forestales. Para esto se utilizan incendios elípticos generados a través de un simulador, los cuales, siguiendo el método de Monte Carlo, son asignados a uno de los patrones representativos de incendio previamente definidos, utilizando la distancia de Pompeiu-Hasudorff. La probabilidad de ocurrencia de los patrones representativos es obtenida al dar cuenta de la cantidad de simulaciones asignada a cada uno de ellos. Para dar con un algoritmo que permitiera utilizar los recursos computacionales de forma eficiente se implementaron distintos métodos para el cálculo de la distancia de Pompeiu-Hausdorff, además de utilizar múltiples procesadores en paralelo cuando esto fuese posible. Cinco métodos fueron implementados, los cuales son definidos utilizando las propiedades geométricas de las elipses para lograr resolver el problema de optimización implícito. El método que logra dar con los resultados más exactos para la distancia hace uso de optimización cónica, mientras que el más rápido calcula la distancia entre cada uno de los vértices de una elipse discretizada. Haciendo uso de estos dos métodos, se genera una estrategia multi etapa para el cálculo de la distancia de Pompeiu-Hasdorff entre dos elipses que es eficiente y precisa. La estabilidad de los resultados obtenidos para 200 patrones es lograda luego de 100,000 sampleos, sin embargo, se observaron variaciones muy pequeñas incluso después de 20,000 simulaciones. En conclusión, los intervalos de confianza obtenidos para las probabilidades calculadas dependen de los recursos computacionales con los que se cuente y de las restricciones de tiempo que puedan ser impuestas. La metodología desarrollada entrega a los planificadores forestales una herramienta para analizar la probabilidad de incendio de zonas determinadas, las cuales pueden ser utilizadas en un modelo de optimización bajo incertidumbre que les permita manejar los recursos disponibles de la mejor forma posible.
45
Levin, Rikard. "Uncertainty in risk assessment : contents and modes of communication." Licentiate thesis, Stockholm : Kungliga Tekniska högskolan, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-473.
Full text
46
Santoso, Tjendera. "A comprehensive model and efficient solution algorithm for the design of global supply chains under uncertainty." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/32769.
Full text
47
Renzi, Matthew Joseph. "System architecture decisions under uncertainty : a case study on automotive battery system design." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76579.
Full textAbstract:
Thesis (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, System Design and Management Program, 2012."June 2012." Cataloged from PDF version of thesis.
Includes bibliographical references (p. 63-65).
Flexibility analysis using the Real Options framework is typically utilized on high-level architectural decisions. Using Real Options, a company may develop strategies to mitigate downside risk for future uncertainties while developing upside opportunities. The MIT-Ford Alliance has extended the techniques of flexibility analysis beyond high-level architecture to core product design decisions in future vehicle electrification. This thesis provides a methodology for a real-time support framework for developing novel engineering decisions. Risk is high in new product introduction. For hybrid and electric vehicles, market demand and technology forecasts have substantial uncertainty. The uncertainty is anticipated, as the high voltage battery pack hardware and control system architecture will experience multiple engineering development cycles in the next 20 years. Flexibility in product design could mitigate future risk due to uncertainty. By understanding the potential iteration of core technologies, the engineering team can provide flexibility in battery pack voltage monitoring, thermal control, and support software systems to meet future needs. The methodology used in this thesis has been applied in a Ford-MIT Alliance project. The Ford and MIT teams have valued key items within the core technology subsystems and have developed flexible strategies to allow Ford to capture upside potential while protecting against downside risk, with little-to-no extra cost at this early stage of development. A novel voltage monitoring technique and a unique flexible thermal control strategy have been identified and are under consideration by Ford. The flexibility methodology provided motivation and support for unique decisions made during product design by the Ford team.
by Matthew J. Renzi.
S.M.in Engineering and Management
48
Thurner, Marc-Oliver. "Foreign expansion under uncertainty : the case of multinational companies in China /." [St. Gallen] : [s.n.], 2005. http://www.gbv.de/dms/zbw/503132659.pdf.
Full text
49
Kim, Sean Hay. "Development of robust building energy demand-side control strategy under uncertainty." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41115.
Full textAbstract:
The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy.
The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance.
This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions.
Uniqueness and superiority of the proposed robust demand-side controls are found as below:
a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis.
b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario.
c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties.
The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.
50
Sae-lim, Napussakorn. "Long-term planning of a solid waste management system under uncertainty, an inexact optimization approach." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0003/MQ45341.pdf.
Full text