Stochastické programování a optimalizace jsou mocnými nástroji pro řešení široké škály inženýrských problémů zahrnujících neurčitost. Algoritmus progressive hedging je efektivní dekompoziční metoda určená pro řešení scénářových stochastických úloh. Z důvodu vertikální dekompozice je možno tento algoritmus implementovat paralelně, čímž lze významně ušetřit výpočetní čas a ostatní prostředky. Teoretická část této diplomové práce se zabývá matematickým a zejména pak stochastickým programováním a detailně popisuje algoritmus progressive hedging. V praktické části je navržena a diskutována původní paralelní implementace algoritmu progressive hedging, která je pak otestována na jednoduchých úlohách. Dále je uvedená paralelní implementace použita pro řešení inženýrského problému plynulého odlévání ocelové bramy a na závěr jsou získané výsledky zhodnoceny.

Optimalizace toku v síti je klasickou aplikací matematického programování. Tyto modely mají, mimo jiné, široké uplatnění také v logistice, kde se tak snažíme docílit optimálního rozdělení dopravy, např. vzhledem k maximalizaci zisku, či minimalizaci nákladů. Toto pojetí ovšem často problém idealizuje, poněvadž předpokládá existenci jediného rozhodovatele. Takový přístup je možný ve striktně organizovaných sítích jako např. v logistických sítích přepravních společností, železničních sítích či armádním zásobování. Úloha ''Traffic Assignment Problem'' (TAP) se zaměřuje na dopady teorie her na optimalizaci toku, tj. zkoumá vliv více rozhodovatelů na celkové využití sítě. V práci se zaobíráme úlohou TAP s působením náhodných vlivů, k čemuž využíváme metod stochastické a vícestupňové optimalizace. Dále zkoumáme možnosti zlepšení stávajícího využití sítě za rozhodnutí autoritativního rozhodovatele, kterému je umožněn zásah do samotné struktury sítě, k čemuž využíváme víceúrovňové programování.

Multi-Stage Stochastic Programming with CVaR: Modeling, Algorithms and Robustness RNDr. Václav Kozmík Abstract: We formulate a multi-stage stochastic linear program with three different risk measures based on CVaR and discuss their properties, such as time consistency. The stochastic dual dynamic programming algorithm is described and its draw- backs in the risk-averse setting are demonstrated. We present a new approach to evaluating policies in multi-stage risk-averse programs, which aims to elimi- nate the biggest drawback - lack of a reasonable upper bound estimator. Our approach is based on an importance sampling scheme, which is thoroughly ana- lyzed. A general variance reduction scheme for mean-risk sampling with CVaR is provided. In order to evaluate robustness of the presented models we extend con- tamination technique to the case of large-scale programs, where a precise solution cannot be obtained. Our computational results are based on a simple multi-stage asset allocation model and confirm usefulness of the presented procedures, as well as give additional insights into the behavior of more complex models. Keywords: Multi-stage stochastic programming, stochastic dual dynamic programming, im- portance sampling, contamination, CVaR

The thesis deals with a multistage stochastic model and its application to a number of practical problems. Special attention is devoted to the case where a random element follows an autoregressive sequence and the constraint sets correspond to the individual probability constraints. For this case conditions under which is the problem well-defined are specified. Further, the approximation of the problem and its convergence rate under the empirical estimate of the distribution function is analyzed. Finally, an example of the investment in financial instruments is solved, which is defined as a two-stage stochastic programming problem with the probability constraint and a random element following an autoregressive sequence. Powered by TCPDF (www.tcpdf.org)

Market makers such as large banks are exposed to market risk in fixed income by acting as a counterparty for customers that enter swap contracts. This master thesis addresses the problem of creating a cost-effective hedge for a realistic swap book of a market maker in a multiple yield curve setting. The proposed hedge model is the two-stage stochastic optimisation problem created by Blomvall and Hagenbjörk (2020). Systematic term structure innovations (components) are estimated using six different component models including principal component analysis (PCA), independent component analysis (ICA) and rotations of principal components. The component models are evaluated with a statistical test that uses daily swap rate observations from the European swap market. The statistical test shows that for both FRA and IRS contracts, a rotation of regular principal components is capable of a more accurate description of swap rate innovations than regular PCA. The hedging model is applied to an FRA and an IRS swap book separately, with daily rebalancing, over the period 2013-06-21 to 2021-05-11. The model produces a highly effective hedge for the tested component methods. However, replacing the PCA components with improved components does not improve the hedge. The study is conducted in collaboration with two other master theses, each done at separate banks. This thesis is done in collaboration with Swedbank and the simulated swap book is based on the exposure of a typical swap book at Swedbank, which is why the European swap market is studied.

For ensuring network survivability in case of single component failures many routing protocols provide a primary and a back up routing path for each origin destination pair. We address the problem of selecting these paths such that in the event of multiple failures, occuring with given probabilities, the total loss in routable demand due to both paths being intersected is small with high probability. We present a chance constraint model and solution approaches based on an explicit integer programming formulation, a robust formulation and a cutting plane approach that yield reasonably good solutions assuming that the failures are caused by at most two elementary events, which may each affect several network components.

In this dissertation, we study two important problems related to additive manufacturing (AM). In the first part, we investigate the economic feasibility of using AM to fabricate biomedical implants at the sites of hospitals AM versus traditional manufacturing (TM). We propose a cost model to quantify the supply-chain level costs associated with the production of biomedical implants using AM technology, and formulate the problem as a two-stage stochastic programming model, which determines the number of AM facilities to be established and volume of product flow between manufacturing facilities and hospitals at a minimum cost. We use the sample average approximation (SAA) approach to obtain solutions to the problem for a real-world case study of hospitals in the state of Mississippi. We find that the ratio between the unit production costs of AM and TM (ATR), demand and product lead time are key cost parameters that determine the economic feasibility of AM. In the second part, we investigate the AM facility deployment approaches which affect both the supply chain network cost and the extent of benefits derived from AM. We formulate the supply chain network cost as a continuous approximation model and use optimization algorithms to determine how centralized or distributed the AM facilities should be and how much raw materials these facilities should order so that the total network cost is minimized. We apply the cost model to a real-world case study of hospitals in 12 states of southeastern USA. We find that the demand for biomedical implants in the region, fixed investment cost of AM machines, personnel cost of operating the machines and transportation cost are the major factors that determine the optimal AM facility deployment configuration. In the last part, we propose an enhanced sample average approximation (eSAA) technique that improves the basic SAA method. The eSAA technique uses clustering and statistical techniques to overcome the sample size issue inherent in basic SAA. Our results from extensive numerical experiments indicate that the eSAA can perform up to 699% faster than the basic SAA, thereby making it a competitive solution approach of choice in large scale stochastic optimization problems.

[pt] Finanças corporativas compreendem políticas de investimento, financiamento e dividendo cujo objetivo é maximizar o valor do acionista. Em particular, os resultados de empresas produtoras de commodities e, consequentemente, o valor para seus acionistas estão sujeitos a alta volatilidade, decorrentes da variação dos preços destes produtos no mercado global. Entretanto, o risco dessa variação pode ser mitigado ao se explorar o amplo mercado de derivativos que, em geral, está disponível para commodities. Este trabalho propõe calcular o acréscimo de valor que uma empresa produtora de commodities pode fornecer ao seu acionista pelo uso de uma política ótima de gestão de derivativos, por meio da compra ou venda de contratos a termo. Para tanto, busca maximizar o retorno aos acionistas via dividendos em um ambiente avesso a risco. O modelo assume que o preço da commodity segue um processo de Markov de estados discretos. Como o modelo é aplicado em vários estágios, o problema torna-se bastante complexo, sendo necessário usar um método de decomposição para obter a solução, sendo assim, utilizou-se o método conhecido como programação dual dinâmica estocástica. Os resultados demonstram que, ao comercializar contratos forward, uma empresa aumenta o valor percebido pelo acionista, medido pelo pagamento de dividendos, para qualquer nível de aversão a risco. A média de acréscimo de valor, considerando diferentes níveis de aversão a risco e uma premissa de precificação não viesada, é superior a 320 por cento quando comparado a empresas que não possuem acesso a tais instrumentos. Além de medir o acréscimo de valor, analisou-se também quais os fatores determinantes para a política ótima de gestão de derivativos. Foi possível identificar que a política de gestão de derivativos é muito determinada pelos preços, que por sua vez estão associados ao estado da cadeia de Markov vigente em cada estágio. / [en] Corporate finance comprises investment, financing and dividend policies aimed at maximizing shareholder value. In particular, the results of commodity producers and, consequently, the value to their shareholders are subject to high volatility, resulting from the variation of prices of these products in the global market. However, the risk of this variation can be mitigated by exploiting the broad derivatives market that is generally available for commodities. This work proposes to calculate the value increase that a commodity-producing company can provide to its shareholders through the use of an optimal derivatives management policy, by buying or selling forward contracts. To this end, it seeks to maximize shareholder returns via dividends in a risk-averse environment. The model assumes that the commodity price follows a discrete state Markov process. Since the model is applied in several stages, the problem becomes quite complex, and it is necessary to use a decomposition method to obtain the solution, so we used the method known as stochastic dynamic dual programming. The results show that by trading forward contracts, a company increases the value perceived by the shareholder, measured by the payment of dividends, to any level of risk aversion. The average value increase, considering different levels of risk aversion and an unbiased pricing assumption, is higher than 320 per cent when compared to companies that do not have access to such instruments. In addition to measuring the value increase, we also analyzed which factors determine the optimal derivatives management policy. It was possible to identify that the derivatives management policy is very determined by the prices, which in turn are associated with the state of the Markov chain in force at each stage.

No description available.

No description available.