The thesis contains the results of a reseach project on application of mathematical programming methods to short-term operation planning of large hydrothermal power systems. The project was aimed at devoeloping efficient solution techniques that are practially applicable to large systems. The problem is modeled as a large mixed integer program. / This thesis contains the results of a research project on application of mathematical programming methods to short-term operation planning of large hydrothermal power systems. The work was carried out at the Department of Electric Power System Engineering of the Royal Institute of Technology, Stockholm, Sweden.   The project was aimed at developing efficient solution techniques that are practically applicable to large scale power systems. The thesis consists of seven chap­ters and four appendices.   The increasing importance and the magnitude of the expenditures associated with it have created an urgent necessity to operate the electric energy systems in an optimal economic manner. The optimal operation planning, as explained in chapter 1, can be divided into several subproblems which are more computationally manageable. The short-term operation planning contains two of these subproblems, namely; weekly and daily operation planning.   The problem, as modeled in chapter 2 for systems with a considerable amount of hydro, is a large mixed integer program. The objective for this problem is the produc­tion cost of the thermal plants. The optimization hori­zon varies from one week to one day, and the discretiza­tion intervals are normally chosen between one to several hours.   In chapter 3, Lagrangian relaxation technique and Benders' method are introduced to decompose the problem with re­spect to hydro and thermal systems. This makes it poss­ible to exploit the special characteristics of each system.   The hydro problem is a large linear program with embedded network structure. In chapter 4, several solution techniques are introduced that exploit this special structure of the large number of constraints involved. The small nonlinearities of hydro problem and· head variation are also treated in this chapter.   The thermal problem involves integer variables. In cha­ter 5, the special structure of this problem is consider­ed, which results in a considerable amount of reductions. Branch and bound, shortest path, and discrete dynamic programming methods are considered for solution of thermal system. This chapter is extended to consider hydro­thermal power system with low amounts of hydro.   Chapter 6 concerns network labeling system, network flow algorithms, and sparsity techniques, which were considered in the implementation of the algorithms.   Finally, the test results and conclusions from application of different techniques are considered and discussed in chapter 7. The Swedish System has been used to prove the applicability and efficiency of the developed techniques. The short-term model can be used in operation, as an engineering tool for decision making, and in planning, to analyze alternative planning schemes. / <p>QC 20161206</p>

Computational modeling is of fundamental significance in mapping possible disease spread, and designing strategies for its mitigation. Conventional contact networks implement the simulation of interactions as random occurrences, presenting public health bodies with a difficult trade off between a realistic model granularity and robust design of intervention strategies. Recently, researchers have been investigating the use of agent-based models (ABMs) to embrace the complexity of real world interactions. At the same time, theoretical approaches provide epidemiologists with general optimization models in which demographics are intrinsically simplified. The emerging study of affiliation networks and co-affiliation networks provide an alternative to such trade off. Co-affiliation networks maintain the realism innate to ABMs while reducing the complexity of contact networks into distinctively smaller k-partite graphs, were each partition represent a dimension of the social model. This dissertation studies the optimization of intervention strategies for infectious diseases, mainly distributed in school systems. First, concepts of synthetic populations and affiliation networks are extended to propose a modified algorithm for the synthetic reconstruction of populations. Second, the definition of multi-coaffiliation networks is presented as the main social model in which risk is quantified and evaluated, thereby obtaining vulnerability indications for each school in the system. Finally, maximization of the mitigation coverage and minimization of the overall cost of intervention strategies are proposed and compared, based on centrality measures.

The main objective of the thesis is to analyse impact of wavelet covariance estimation in the context of Markowitz mean-variance portfolio selection. We use a rolling window to apply maximum overlap discrete wavelet transform to daily returns of 28 companies from DJIA 30 index. In each step, we compute portfolio weights of global minimum variance portfolio and use those weights in the out-of- sample forecasts of portfolio returns. We let rebalancing period to vary in order to test influence of long-term and short-term traders. Moreover, we test impact of different wavelet filters including Haar, D4 and LA8. Results reveal that only portfolios based on the first scale wavelet covariance produce significantly higher returns than portfolios based on the whole sample covariance. The disadvantage of those portfolios is higher riskiness of returns represented by higher Value at Risk and Expected Shortfall, as well as higher instability of portfolio weights represented by shorter period that is required for portfolio weights to significantly differ. The impact of different wavelet filters is rather minor. The results suggest that all relevant information about the financial market is contained in the first wavelet scale and that the dynamics of this scale is more intense than the dynamics of the whole market.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd W. Easton / The possibility of becoming a millionaire attracts over 200,000 daily fantasy sports (DFS) contest entries each Sunday of the NFL season. Millions of people play fantasy sports and the companies sponsoring daily fantasy sports are worth billions of dollars. This thesis develops optimization models for daily fantasy sports with an emphasis on tiered contests. A tiered contest has many different payout values, including the highly sought after million-dollar prize. The primary contribution of this thesis is the first model to optimize the expected payout of a tiered DFS contest. The stochastic integer program, MMIP, takes into account the possibility that selected athletes will earn a distribution of fantasy points, rather than a single predetermined value. The players are assumed to have a normal distribution and thus the team’s fantasy points is a normal distribution. The standard deviation of the team’s performance is approximated through a piecewise linear function, and the probabilities of earning cumulative payouts are calculated. MMIP solves quickly and easily fits the majority of daily fantasy sports contests. Additionally, daily fantasy sports have landed in a tense political climate due to contestants hopes of winning the million-dollar prize. Through two studies that compare the performance of randomly selected fantasy teams with teams chosen by strategy, this thesis conclusively determines that daily fantasy sports are not games of chance and should not be considered gambling. Besides creating the first optimization model for DFS tiered contests, this thesis also provides methods and techniques that can be applied to other stochastic integer programs. It is the author’s hope that this thesis not only opens the door for clever ways of modeling, but also inspires sports fans and teams to think more analytically about player selection.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Todd Easton / Heuristics are often implemented to find better solutions to computationally challenging problems. Heuristics use varying techniques to search for quality solutions. Several optimization heuristics have drawn inspiration from real world practices. Ant colony optimization mimics ants in search of food. Genetic algorithms emulate traits being passed from a parent to a child. Simulated annealing imitates annealing metal. This thesis presents a new variable neighborhood search optimization heuristic, fútbol Strategies applied to Optimize Combinatorial problems to Create Efficient Results, which is called the SOCCER heuristic. This heuristic mimics fútbol and the closest player to the ball performs his neighborhood search and players are assigned different neighborhoods. The SOCCER heuristic is the first application of variable neighborhood search heuristic that uses a complex structure to select neighborhoods. The SOCCER heuristic can be applied to a variety of optimization problems. This research implemented the SOCCER heuristic for job shop scheduling problems. This implementation focused on creating a quality schedule for a local limestone company. A small computational study shows that the SOCCER heuristic can quickly solve complex job shop scheduling problems with most instances finishing in under an half an hour. The optimized schedules reduced the average production time by 7.27%. This is roughly a 2 day decrease in the number of days required to produce a month’s worth of orders. Thus, the SOCCER heuristic is a new optimization tool that can aid companies and researchers find better solutions to complex problems.

Doctor of Philosophy / Department of Mechanical and Nuclear Engineering / Donald Fenton / Combustion turbine performance deterioration, quantified by loss of system power, is an artifact of increased inlet air temperature and continuous degradation of the machine. Furthermore, the combustion turbine operator has to meet ever changing stricter emission levels. Different technologies exist to mitigate the impact of performance loss and meeting the emission standard. However an upgrade using one or more of the available technologies has associated capital and operating costs. Thus, there is a need for a tool that can evaluate power boosting and emission control technologies in concert with the machine maintenance strategy. This dissertation provides the turbine operator with a new and novel tool to examine each of the upgrades and determine its suitability both from the cost and technical stand point. The main contribution of this dissertation is a tool-kit called the Combustion Turbine Operation and Optimization Model (CTOOM) that can evaluate both power-boosting and emission control technologies. It also includes a machine maintenance model to account for degradation recovery. The tool-kit is made up a system level thermodynamic optimization solver (CTOOM-OPTIMIZE) and two one-dimensional, mean-line, aero-thermodynamic component level solvers for the compressor (CTOOMCOMP1DPERF) and the turbine (CTOOMTURB1DPERF) sections. In this work, the cogeneration system as given by the classical CGAM problem was used for system level optimization. The cost function was modified to include the cost of emissions while the maintenance cost of the combustion turbine was separated from the capital cost to include a degradation recovery model. Steam injection was evaluated for NO[subscript]x abatement, power boosting was examined by both the use of inlet air cooling and steam injection, and online washing was used for degradation recovery. Based on the cost coefficients used, it was seen that including the cost of emissions impact resulted in a significant increase in the operational cost. The outcomes of the component level solvers were compressor and turbine performance maps. It was demonstrated that these maps could be used to integrate the components with the system level information.

Magíster en Ciencias, Mención Computación / Este trabajo de tesis está dedicado al diseño e implementación de algoritmos aproximados que permiten explorar las mejores soluciones para el problema de Clustering Balanceado, el cual consiste en dividir un conjunto de n puntos en k clusters tal que cada cluster tenga como m ́ınimo ⌊ n ⌋ puntos, k y éstos deben estar lo más cercano posible al centroide de cada cluster. Estudiamos los algoritmos existentes para este problema y nuestro análisis muestra que éstos podrían fallar en entregar un resultado óptimo por la ausencia de la evaluación de los resultados en cada iteración del algoritmo. Entonces, recurrimos al concepto de Particles Swarms, que fue introducido inicialmente para simular el comportamiento social humano y que permite explorar todas las posibles soluciones de manera que se aproximen a la óptima rápidamente. Proponemos cuatro algoritmos basado en Particle Swarm Optimization (PSO): PSO-Hu ́ngaro, PSO-Gale-Shapley, PSO-Aborci ́on-Punto-Cercano y PSO-Convex-Hull, que aprovechan la característica de la generación aleatoria de los centroides por el algoritmo PSO, para asignar los puntos a estos centroides, logrando una solución más aproximada a la óptima. Evaluamos estos cuatro algoritmos con conjuntos de datos distribuidos en forma uniforme y no uniforme. Se encontró que para los conjuntos de datos distribuidos no uniformemente, es impredecible determinar cuál de los cuatro algoritmos propuestos llegaría a tener un mejor resultado de acuerdo al conjunto de métricas (intra-cluster-distancia, índice Davies-Doublin e índice Dunn). Por eso, nos concentramos con profundidad en el comportamiento de ellos para los conjuntos de datos distribuidos en forma uniforme. Durante el proceso de evaluación se descubrió que la formación de los clusters balanceados de los algoritmos PSO-Absorcion-Puntos-Importantes y PSO-Convex-Hull depende fuertemente del orden con que los centroides comienzan a absorber los puntos más cercanos. En cambio, los algoritmos PSO-Hungaro y PSO-Gale-Shapley solamente dependen de los centroides generados y no del orden de los clusters a crear. Se pudo concluir que el algoritmo PSO-Gale-Shapley presenta el rendimiento menos bueno para la creación de clusters balanceados, mientras que el algoritmo PSO-Hungaro presenta el rendimiento más eficiente para lograr el resultado esperado. Éste último está limitado al tamaño de los datos y la forma de distribución. Se descubrió finalmente que, para los conjuntos de datos de tamaños grandes, independiente de la forma de distribución, el algoritmo PSO-Convex-Hull supera a los demás, entregando mejor resultado según las métricas usadas.

In this article a new branch & bound method is described. It uses an artificial target to improve its bounding capabilities. Therefore the new approach is faster compared to the classical one. It is applied to the stochastic problem of optimal scenario tree reduction. The aspects of global optimization are emphasized here. All necessary components for that problem are developed and some experimental results underline the benefits of the new approach. (author's abstract) / Series: Working Papers on Information Systems, Information Business and Operations

<p>Storage systems play a vital part in modern IT systems. As the volume of data grows explosively and greater requirement on storage performance and reliability is put forward, effective and efficient design and operation of storage systems become increasingly complicated. </p><p>Such efforts would benefit significantly from the availability of quantitative analysis techniques that facilitate comparison of different system designs and configurations and provide projection of system behavior under potential operational scenarios. The techniques should be able to capture the system details that are relevant to the system measures of interest with adequate accuracy, and they should allow efficient solution so that they can be employed for multiple scenarios and for dynamic system reconfiguration. </p><p>This dissertation develops a set of quantitative analysis methods for modern storage systems using stochastic modeling techniques. The presented models cover several of the most prevalent storage technologies, including RAID, cloud storage and replicated storage, and investigate some major issues in modern storage systems, such as storage capacity planning, provisioning and backup planning. Quantitative investigation on important system measures such as reliability, availability and performance is conducted, and for this purpose a variety of modeling formalisms and solution methods are employed based on the matching of the underlying model assumptions and nature of the system aspects being studied. One of the primary focuses of the model development is on solution efficiency and scalability of the models to large systems. The accuracy of the developed models are validated through extensive simulation.</p> / Dissertation

The municipality and county administrations of Uppsala are planning a project for coordination of transports to their institutions and services across Uppsala County, which are today done separately. This report aims to analyze what the profits of such coordination might be, in terms of environmental care, economic and social aspects, given the resources available today. This is done by creating optimization models for both separate and coordinated transports, based on previously completed order tables, and comparing the results. The final results show at best a 13% improvement in driving distance and a 7% improvement in driver work time, which transfers into 172 000 SEK savings yearly and significant reductions in CO2-emissions while easing workload during high intensity days. Changes in the transport fleet might increase this to up to 272 000 SEK while possibly further reducing emissions but without creating significant increase in workload for drivers.