A incerteza é um elemento fundamental da realidade. Então, torna-se natural a busca por métodos que nos permitam representar o desconhecido em termos matemáticos. Esses problemas originam uma grande classe de programas probabilísticos reconhecidos como modelos de programação estocástica. Eles são mais realísticos que os modelos determinísticos, e tem por objetivo incorporar a incerteza em suas definições. Essa tese aborda os problemas probabilísticos da classe de problemas de multi-estágio com incerteza e com restrições probabilísticas e com restrições probabilísticas conjuntas. Inicialmente, nós propomos um modelo de administração de ativos e passivos multi-estágio estocástico para a indústria de fundos de pensão brasileira. Nosso modelo é formalizado em conformidade com a leis e políticas brasileiras. A seguir, dada a relevância dos dados de entrada para esses modelos de otimização, tornamos nossa atenção às diferentes técnicas de amostragem. Elas compõem o processo de discretização desses modelos estocásticos Nós verificamos como as diferentes metodologias de amostragem impactam a solução final e a alocação do portfólio, destacando boas opções para modelos de administração de ativos e passivos. Finalmente, nós propomos um “framework” para a geração de árvores de cenário e otimização de modelos com incerteza multi-estágio. Baseados na tranformação de Knuth, nós geramos a árvore de cenários considerando a representação filho-esqueda, irmão-direita o que torna a simulação mais eficiente em termos de tempo e de número de cenários. Nós também formalizamos uma reformulação do modelo de administração de ativos e passivos baseada na abordagem extensiva implícita para o modelo de otimização. Essa técnica é projetada pela definição de um processo de filtragem com “bundles”; e codifciada com o auxílio de uma linguagem de modelagem algébrica. A eficiência dessa metodologia é testada em um modelo de administração de ativos e passivos com incerteza com restrições probabilísticas conjuntas. Nosso framework torna possível encontrar a solução ótima para árvores com um número razoável de cenários. / Uncertainty is a key element of reality. Thus, it becomes natural that the search for methods allows us to represent the unknown in mathematical terms. These problems originate a large class of probabilistic programs recognized as stochastic programming models. They are more realistic than deterministic ones, and their aim is to incorporate uncertainty into their definitions. This dissertation approaches the probabilistic problem class of multistage stochastic problems with chance constraints and joint-chance constraints. Initially, we propose a multistage stochastic asset liability management (ALM) model for a Brazilian pension fund industry. Our model is formalized in compliance with the Brazilian laws and policies. Next, given the relevance of the input parameters for these optimization models, we turn our attention to different sampling models, which compose the discretization process of these stochastic models. We check how these different sampling methodologies impact on the final solution and the portfolio allocation, outlining good options for ALM models. Finally, we propose a framework for the scenario-tree generation and optimization of multistage stochastic programming problems. Relying on the Knuth transform, we generate the scenario trees, taking advantage of the left-child, right-sibling representation, which makes the simulation more efficient in terms of time and the number of scenarios. We also formalize an ALM model reformulation based on implicit extensive form for the optimization model. This technique is designed by the definition of a filtration process with bundles, and coded with the support of an algebraic modeling language. The efficiency of this methodology is tested in a multistage stochastic ALM model with joint-chance constraints. Our framework makes it possible to reach the optimal solution for trees with a reasonable number of scenarios.

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.

This work presents an empirical analysis of popular scenario generation methods for stochastic optimization, including quasi-Monte Carlo, moment matching, and methods based on probability metrics, as well as a new method referred to as Voronoi cell sampling. Solution quality is assessed by measuring the error that arises from using scenarios to solve a multi-dimensional newsvendor problem, for which analytical solutions are available. In addition to the expected value, the work also studies scenario quality when minimizing the expected shortfall using the conditional value-at-risk. To quickly solve problems with millions of random parameters, a reformulation of the risk-averse newsvendor problem is proposed which can be solved via Benders decomposition. The empirical analysis identifies Voronoi cell sampling as the method that provides the lowest errors, with particularly good results for heavy-tailed distributions. A controversial finding concerns evidence for the ineffectiveness of widely used methods based on minimizing probability metrics under high-dimensional randomness.

Asset and Liability Management (ALM) models have become well established decision tools for pension funds. ALMs are commonly modelled as multi-stage, in which a large terminal wealth is required, while at intermediate time periods, constraints on the funding ratio, that is, the ratio of assets to liabilities, are imposed. Underfunding occurs when the funding ratio is too low; a target value for funding ratios is pre-specified by the decision maker. The risk of underfunding has been usually modelled by employing established risk measures; this controls one single aspect of the funding ratio distributions. For example, controlling the expected shortfall below the target has limited power in controlling shortfall under worst-case scenarios. We propose ALM models in which the risk of underfunding is modelled based on the concept of Second Order Stochastic Dominance (SSD). This is a criterion of ranking random variables - in our case funding ratios - that takes the entire distributions of interest into account and works under the widely accepted assumptions of decision makers being rational and risk averse. In the proposed SSD models, investment decisions are taken such that the resulting short-term distribution of the funding ratio is non-dominated with respect to SSD, while a constraint is imposed on the expected terminal wealth. This is done by considering progressively larger tails of the funding ratio distribution and considering target levels for them; a target distribution is thus implied. Different target distributions lead to different SSD efficient solutions. Improved distributions of funding ratios may be thus achieved, compared to the existing risk models for ALM. This is the first contribution of this thesis. Interesting results are obtained in the special case when the target distribution is deterministic, specified by one single outcome. In this case, we can obtain equivalent risk minimisation models, with risk defined as expected shortfall or as worst case loss. This represents the second contribution. The third contribution is a framework for scenario generation based on the "Birth, Immigration, Death, Emigration" (BIDE) population model and the Empirical copula; the scenarios are used to evaluate the proposed models and their special cases both in-sample and out-of-sample. As an application, we consider the planning problem of a large DB pension fund in Saudi Arabia.

Central Clearing Counterparties play a crucial role in financial markets, requiring robust risk management practices to ensure operational stability. A growing emphasis on risk analysis and stress testing from regulators has led to the need for sophisticated tools that can model extreme but plausible market scenarios. This thesis presents a method leveraging Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP) to construct an independent scenario generator capable of modeling and generating return distributions for financial markets. The developed method utilizes two primary components: the WGAN-GP model and a novel scenario selection strategy. The WGAN-GP model approximates the multivariate return distribution of stocks, generating plausible return scenarios. The scenario selection strategy employs lower and upper bounds on Euclidean distance calculated from the return vector to identify, and select, extreme scenarios suitable for stress testing clearing members' portfolios. This approach enables the extraction of extreme yet plausible returns. This method was evaluated using 25 years of historical stock return data from the S&P 500. Results demonstrate that the WGAN-GP model effectively approximates the multivariate return distribution of several stocks, facilitating the generation of new plausible returns. However, the model requires extensive training to fully capture the tails of the distribution. The Euclidean distance-based scenario selection strategy shows promise in identifying extreme scenarios, with the generated scenarios demonstrating comparable portfolio impact to historical scenarios. These results suggest that the proposed method offers valuable tools for Central Clearing Counterparties to enhance their risk management. / Centrala motparter spelar en avgörande roll i dagens finansmarknad, vilket innebär att robusta riskhanteringsrutiner är nödvändiga för att säkerställa operativ stabilitet. Ökande regulatoriskt tryck för riskanalys och stresstestning från tillsynsmyndigheter har lett till behovet av avancerade verktyg som kan modellera extrema men troliga marknadsscenarier. I denna uppsats presenteras en metod som använder Wasserstein Generative Adversarial Networks med Gradient Penalty (WGAN-GP) för att skapa en oberoende scenariogenerator som kan modellera och generera avkastningsfördelningar för finansmarknader. Den framtagna metoden består av två huvudkomponenter: WGAN-GP-modellen och en scenariourvalstrategi. WGAN-GP-modellen approximerar den multivariata avkastningsfördelningen för aktier och genererar möjliga avkastningsscenarier. Urvalsstrategin för scenarier använder nedre och övre gränser för euklidiskt avstånd, beräknat från avkastningsvektorn, för att identifiera och välja extrema scenarier som kan användas för att stresstesta clearingmedlemmars portföljer. Denna strategi gör det möjligt att erhålla nya extrema men troliga avkastningar. Metoden utvärderas med 25 års historisk aktieavkastningsdata från S&P 500. Resultaten visar att WGAN-GP-modellen effektivt kan approximera den multivariata avkastningsfördelningen för flera aktier och därmed generera nya möjliga avkastningar. Modellen kan dock kräva en omfattande mängd träningscykler (epochs) för att fullt ut fånga fördelningens svansar. Scenariurvalet baserat på euklidiskt avstånd visade lovande resultat som ett urvalskriterium för extrema scenarier. De genererade scenarierna visar en jämförbar påverkan på portföljer i förhållande till de historiska scenarierna. Dessa resultat tyder på att den föreslagna metoden kan erbjuda värdefulla verktyg för centrala motparter att förbättra sin riskhantering.

Exploration and production (E&P) involves the upstream activities from looking for promising reservoirs to extracting oil and selling it to downstream companies. E&P is the most profitable business in the oil industry. However, it is also the most capital-intensive and risky. Hence, the proper assessment of E&P projects with effective management of uncertainties is crucial to the success of any upstream business. This dissertation is concentrated on developing portfolio optimization models to manage E&P projects. The idea is not new, but it has been mostly restricted to the conceptual level due to the inherent complications to capture interactions among projects. We disentangle the complications by modeling the project portfolio optimization problem as multistage stochastic programs with mixed integer programming (MIP) techniques. Due to the disparate nature of uncertainties, we separately consider explored and unexplored oil fields. We model portfolios of real options and portfolios of decision trees for the two cases, respectively. The resulting project portfolio models provide rigorous and consistent treatments to optimally balance the total rewards and the overall risk. For explored oil fields, oil price fluctuations dominate the geologic risk. The field development process hence can be modeled and assessed as sequentially compounded options with our optimization based option pricing models. We can further model the portfolio of real options to solve the dynamic capital budgeting problem for oil projects. For unexplored oil fields, the geologic risk plays the dominating role to determine how a field is optimally explored and developed. We can model the E&P process as a decision tree in the form of an optimization model with MIP techniques. By applying the inventory-style budget constraints, we can pool multiple project-specific decision trees to get the multistage E&P project portfolio optimization (MEPPO) model. The resulting large scale MILP is efficiently solved by a decomposition-based primal heuristic algorithm. The MEPPO model requires a scenario tree to approximate the stochastic process of the geologic parameters. We apply statistical learning, Monte Carlo simulation, and scenario reduction methods to generate the scenario tree, in which prior beliefs can be progressively refined with new information. / text

Submitted by Napoleana Barros Martins (napoleana_martins@hotmail.com) on 2016-08-01T15:04:07Z No. of bitstreams: 1 Dissertação Adão Marques dos Santos Cardoso.pdf: 5573273 bytes, checksum: 8f40811743ab6e0dd181e74f98bd4c97 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-08-26T13:19:46Z (GMT) No. of bitstreams: 1 Dissertação Adão Marques dos Santos Cardoso.pdf: 5573273 bytes, checksum: 8f40811743ab6e0dd181e74f98bd4c97 (MD5) / Approved for entry into archive by Divisão de Documentação/BC Biblioteca Central (ddbc@ufam.edu.br) on 2016-08-26T13:22:36Z (GMT) No. of bitstreams: 1 Dissertação Adão Marques dos Santos Cardoso.pdf: 5573273 bytes, checksum: 8f40811743ab6e0dd181e74f98bd4c97 (MD5) / Made available in DSpace on 2016-08-26T13:22:36Z (GMT). No. of bitstreams: 1 Dissertação Adão Marques dos Santos Cardoso.pdf: 5573273 bytes, checksum: 8f40811743ab6e0dd181e74f98bd4c97 (MD5) Previous issue date: 2015-12-23 / Não informada / This study aims the creation of a practical activity of teaching model applicable to the Technical Course in Public Services of the Center of Technological Education of Amazonas (CETAM) in the distance education, held in twelve municipalities of Amazonas state, planned with the use of Bloom's Taxonomy, aiming to achieve the highest cognitive levels. Considering the regional characteristics, it was developed and implemented an educational activity focused on environmental context lived by the students, combining the concepts acquired in the virtual environment with the playfulness of a film; the development of an interdisciplinary project to generate a representative scenario of the knowledge acquired; project execution in a themed room format and ending with the presentation of an evaluation report. For this, the methodology used research was a study of qualitative and quantitative, the case study method, exploratory in nature, in which we analyzed the result of a planned activity to practice in distance education. It was used as a source, a structured survey questionnaire, with closed and open questions, based on the Likert scale and SERVQUAL, quantified and represented in tables and graphs. The data analysis was used for descriptive statistics, with absolute frequency calculation, relative and average, and inferential analysis. Therefore, this research emerges from the challenge of developing manufacturing activities from theory into practice in teaching distance mode. Thus aimed to analyze the results of a case, as regards its contribution to educational quality, improving learning course participants and therefore of their training. Bloom's Taxonomy gave objectivity to planning education, control of learning and assessing the performance of teacher students, thus enabling improvements in cognitive development. The teaching tools used in the movie and themed room have been evaluated by course participants as able to achieve the proposed objectives. The activity proved to be effective in contributing to the improvement of learning and the student's performance, encouraging self-learning and teamwork, as well as for vocational training of quality. / Este estudo objetiva a criação de um modelo de atividade prática de ensino aplicável ao Curso Técnico em Serviços Públicos do Centro de Educação Tecnológica do Amazonas (CETAM) na modalidade a distância (EaD), realizada em doze municípios do Estado do Amazonas, planejada com o uso da Taxonomia de Bloom, visando atingir os mais altos níveis cognitivos. Considerando as características regionais, foi elaborada e executada uma atividade de ensino com foco na contextualização do ambiente vivido pelos discentes, aliando os conceitos adquiridos no ambiente virtual com o lúdico de um filme. A elaboração de um projeto interdisciplinar para geração de um cenário representativo do conhecimento adquirido; a execução do projeto no formato de uma sala temática e finalizando com a apresentação de um relatório avaliativo. Para isso, a metodologia da pesquisa utilizada foi um estudo do tipo quali-quantitativo, pelo método de estudo de caso, de natureza exploratória, no qual analisa-se o resultado de uma atividade planejada para a prática no EaD. Foi utilizado como fonte, um questionário de pesquisa estruturado, com perguntas fechadas e abertas, baseado na escala Likert e SERVQUAL, quantificadas e representadas em tabelas e gráficos. A análise dos dados realizou-se por estatística descritiva, com cálculo de frequência absoluta, relativa e média, e por análise inferencial. Portanto, esta pesquisa emerge do desafio de desenvolver atividades transformadoras da teoria em prática na modalidade do ensino a distância. Assim, objetivou analisar os resultados de um caso concreto, no que concerne a sua contribuição para a qualidade educacional, a melhoria da aprendizagem dos cursistas e, consequentemente, de sua formação profissional. A Taxonomia de Bloom proporciou objetividade ao planejamento do ensino, ao controle da aprendizagem e a avaliação do desempenho dos cursistas, possibilitando assim melhorias no desenvolvimento cognitivo. As ferramentas didáticas utilizadas, o filme e a sala temática, foram avaliadas pelos cursistas como capazes de atingir os objetivos propostos. A atividade revelou-se eficiente na contribuição para a melhoria da aprendizagem e do desempenho do educando, estímulo a autoaprendizagem e ao trabalho em equipe, bem como para uma formação profissional com qualidade.

La présente thèse s’intéresse aux modèles d’allocation stratégiques d’actifs et à leurs applications pour la gestion des réserves financières des régimes de retraite par répartition, en particulier ceux partiellement provisionnés. L’étude de l’utilité des réserves pour un système par répartition et a fortiori de leur gestion reste un sujet peu exploré. Les hypothèses classiques sont parfois jugées trop restrictives pour décrire l'évolution complexe des réserves. De nouveaux modèles et de nouveaux résultats sont développés à trois niveaux : la génération de scénarios économiques (GSE), les techniques d’optimisation numérique et le choix de l’allocation stratégique optimale dans un contexte de gestion actif-passif (ALM). Dans le cadre de la génération de scénarios économiques et financiers, certains indicateurs de mesure de performance du GSE ont été étudiés. Par ailleurs, des améliorations par rapport à ce qui se pratique usuellement lors de la construction du GSE ont été apportées, notamment au niveau du choix de la matrice de corrélation entre les variables modélisées. Concernant le calibrage du GSE, un ensemble d’outils permettant l’estimation de ses différents paramètres a été présenté. Cette thèse a également accordé une attention particulière aux techniques numériques de recherche de l'optimum, qui demeurent des questions essentielles pour la mise en place d'un modèle d'allocation. Une réflexion sur un algorithme d’optimisation globale d’une fonction non convexe et bruitée a été développée. L’algorithme permet de moduler facilement, au moyen de deux paramètres, la réitération de tirages dans un voisinage des points solutions découverts, ou à l’inverse l’exploration de la fonction dans des zones encore peu explorées. Nous présentons ensuite des techniques novatrices d'ALM basées sur la programmation stochastique. Leur application a été développée pour le choix de l’allocation stratégique d’actifs des régimes de retraite par répartition partiellement provisionnés. Une nouvelle méthodologie pour la génération de l’arbre des scénarios a été adoptée à ce niveau. Enfin, une étude comparative du modèle d’ALM développé avec celui basé sur la stratégie Fixed-Mix a été effectuée. Différents tests de sensibilité ont été par ailleurs mis en place pour mesurer l’impact du changement de certaines variables clés d’entrée sur les résultats produits par notre modèle d’ALM / This thesis focuses on the strategic asset allocation models and on their application for the financial reserve management of a pay-as-you-go (PAYG) retirement schemes, especially those with partial provision. The study of the reserve utility for a PAYG system and of their management still leaves a lot to be explored. Classical hypothesis are usually considered too restrictive for the description of the complex reserve evolution. New models and new results have been developed over three levels : economic scenario generation (ESG), numerical optimization techniques and the choice of optimal strategic asset allocation in the case of an Asset-Liability Management (ALM). For the generation of financial and economic scenarios, some ESG performance indicators have been studied. Also, we detailed and proposed to improve ESG construction, notably the choice of the correlation matrix between modelled variables. Then, a set of tools were presented so that we could estimate ESG parameters variety. This thesis has also paid particular attention to numerical techniques of optimum research, which is an important step for the asset allocation implementation. We developed a reflexion about a global optimisation algorithm of a non convex and a noisy function. The algorithm allows for simple modulating, through two parameters, the reiteration of evaluations at an observed point or the exploration of the noisy function at a new unobserved point. Then, we presented new ALM techniques based on stochastic programming. An application to the strategic asset allocation of a retirement scheme with partial provision is developed. A specific methodology for the scenario tree generation was proposed at this level. Finally, a comparative study between proposed ALM model and Fixed-Mix strategy based model was achieved. We also made a variety of a sensitivity tests to detect the impact of the input values changes on the output results, provided by our ALM model

This report addresses the environmental discourse of the current comprehensive plan of Stockholm, The Walkable City, and in what ways it can be developed following an ecofeminist framework. With a foundation in a critical perspective on current processes of urbanization, ecofeminist theory, and an interest in the potential in utopian thought, this thesis attempts to assist the comprehensive plan in finding alternative ways of approaching the urban development of the city, in search for social and environmental justice through increased citizen participation. Ecofeminists argue that economic growth is conditioned by and enforces colonial and patriarchal relations between humans, and between humans and nature. The current global process of urbanization is an integral part in sustaining the economic growth, making cities an important area to address in search of other relations. Sustainable development has been put forth as a way to relieve the negative social and environmental effects of the economic system, and has informed much urban policy-making. Urban policy, such as comprehensive plans, shape the path of urban development. The main strategy of the current comprehensive plan of Stockholm is ”sustainable growth”. This thesis analyses the components that make out the strategy of sustainable growth in order to understand why it is problematic. Further, it engages in participatory scenario generation using an ecofeminist framework, to find new ideas and paths for a sustainable urban development in Stockholm. The conclusions that are found comprise the positive notion that there are great chances to develop the notion and strategies for sustainability through workshops with stakeholders. While the overall course of development of the scenarios may be too far from our current reality to be easily implemented, there are several suggestions with great potential even in our current context. Furthermore, the results point out important directions in which to develop the plan in a longer perspective, that concern a fundamental restructuring of the political economy along more socially and environmentally sound lines that elevate the interconnectedness of humans and nature. This is a restructuring that makes economic growth according to the current model impossible, but that aims for equality between humans and far-reaching environmental protection. It is also pointed out that the deployment of a multitude of conflicting scenarios in planning would benefit both the urban development and democratic participation. / Denna studie hanterar miljödiskurser i den gällande översiktsplanen för Stockholm, Promenadstaden, och hur de kan utvecklas utefter ett ekofeministiskt ramverk. Med en grund i ett kritiskt perspektiv på samtida urbaniseringsprocesser, ekofeministisk teori, och ett intresse i den potential som vilar i utopiskt tänkande, avser detta arbete vara översiktsplanen behjälplig i att hitta alternativa vägar att närma sig stadens utveckling, på jakt efter social och miljörättvisa genom ökat medborgardeltagande. Ekofeminister menar att ekonomisk tillväst villkoras av och förstärker koloniala och patriarkala relationer mellan människor, och mellan människor och naturen. Den nuvarande globala urbaniseringsprocessen är en grundläggande del i att upprätthålla ekonomisk tillväxt, vilket gör städer till ett viktigt område att hantera ifall nya relationer ska etableras. Hållbar utveckling har framhävts som ett sätt att minska det ekonomiska systemets negativa sociala och miljöeffekter, och har därför haft stort inflytande över policys för stadsutveckling. Policys, som översiktsplaner, formar hur våra städer utvecklas. Den huvudsakliga strategin i Stockholms gällande översiktsplan är ”hållbar tillväxt”. Denna studie analyserar de delar som formar strategin hållbar tillväxt för att förstå varför den är problematisk. Vidare ägnas arbetet åt generering av framtidsscenarier med deltagare utifrån ett ekofeministiskt ramverk, för att finna nya idéer och spår för hållbar stadsutveckling i Stockholm. Slutsatserna omfattar den positiva uppfattningen att det finns goda chanser att utveckla formuleringen av och strategierna för att nå hållbarhet genom workshops med deltagare. Även om den övergripande utvecklingsinriktningen i scenarierna ligger alltför långt ifrån vår nuvarande verklighet för att med lätthet implementeras finns det fortfarande många konkreta förslag med god potential redan idag. Vidare pekar resultaten ut viktiga riktningar för planens utveckling i ett längre perspektiv, vilka har att göra med en grundläggande omstrukturering av den politiska ekonomin i enlighet med en mer socialt och miljömässigt förnuftig riktning, där mänsklighetens och naturens ofrånkomliga sammanlänkning upphöjs. Det är en omstrukturering som gör ekonomisk tillväxt enligt nuvarande modell omöjlig, men som eftersträvar jämlikhet mellan människor och ett långtgående miljöskydd. Det påpekas vidare i rapporten att nyttjandet av motstridiga scenarier i planering skulle bidra både till stadsutveckling och demokratiskt deltagande, där de konflikter planering präglas av blottläggs.

In this dissertation, we develop novel approaches to independently address two issues that are commonly encountered in machine scheduling problems: uncertainty of problem parameters (in particular, due to job processing times), and batching of jobs for processing on capacitated machines. Our approach to address the uncertainty issue regards the indeterminate parameters as random variables, and explicitly considers the resulting variability of a performance measure. To incorporate variability into the schedule selection process, we develop a method to evaluate both the expectation and variance of various performance measures for a given schedule. Our method is based on the use of mixture models to approximate a variety of distribution types. The Expectation-Maximization algorithm of Dempster et al. (1977) is applied to derive mixture models of processing time distributions. Our method, then, utilizes these mixture models to calculate the distributions of other random variables in order to derive the expectation and variance of various scheduling performance measures, assuming that the job sequencing decisions are known a priori. To make our method more computationally efficient, we adapt a mixture reduction method to control the number of mixture components used in the intermediate steps. We apply our method to two different scheduling problems: the job shop makespan scheduling problem and the single machine total weighted tardiness scheduling problem, and compare its performance with that of Monte-Carlo method. The results show the efficacy of our mixture approximation method. It generates fairly accurate results while requiring significantly less CPU times. The proposed method offers a good compromise between the Monte Carlo method, which requires extensive effort, and use of simple normal approximation, which produces lower-quality results. Next, we introduce and demonstrate for the first time in the literature the use of conditional-value-at-risk (CVaR) as a criterion for stochastic scheduling problems in order to obtain risk-averse solutions. This criterion has the tendency of minimizing both the expectation and variance of a performance measure simultaneously, which is an attractive feature in the scheduling area as most of the literature in this area considers the expectation and variance of a performance measure separately. Also, the CVaR has an added advantage of maintaining a linear objective function. We develop a scenario-based mixed integer programming formulation to minimize CVaR for the general scheduling problem involving various performance measures, and employ a decomposition-based approach for its solution. Furthermore, a set of valid inequalities are incorporated to strengthen the relaxed master problem of this decomposition scheme. The proposed approach is demonstrated on the single machine total weighted tardiness scheduling problem. Our computational investigation reveals the efficacy of the proposed decomposition approach and the effectiveness of using the CVaR as an optimization criterion for scheduling problems. Besides providing an exact approach to solve our stochastic scheduling problem, we also develop an efficient heuristic method to enable the use of CVaR for large-sized problems. To that end, we modify the Dynasearch method of Grosso et al. (2004) to minimize CVaR for a stochastic scheduling problem. Furthermore, we extend the application of CVaR to a parallel-machine total weighted tardiness problem. The use of CVaR appears to be quite promising for simultaneously controlling both the expected value and variability of a performance measure in a stochastic scheduling environment. Scenario-based formulations have frequently been used for stochastic scheduling problems. However, the determination of a lower bound can be a time-consuming task for this approach. Next, we develop a new method for scenario generation that is computationally competitive and that assures attainment of an exact lower bound. Our approach is based on discretization of random parameter distributions of job processing times. We use the idea of Recursive Stratified Sampling to partition the probability space, so that the conditional expectations in each region yield scenario-wise parameter values. These scenarios are, then, used to formulate a two-stage stochastic program, which yields a lower bound for the original stochastic problem. We provide theoretical basis of our bounding approach for both the expectation and CVaR objectives. Our discrete bounding method generates exact lower bounds, as against the probabilistic bounds generated by Sample Average Approximation. We also present results of our numerical experimentation to compare the performances of these two approaches in terms of the bound value obtained and the CPU time required. The problem pertaining to integrated batching and scheduling of jobs on capacitated parallel machines that we consider arises in the primary manufacturing sector of a pharmaceutical supply chain. We, first, develop a comprehensive mathematical programming model that can accommodate various realistic features of this problem. These features include batch production, sequence-dependent setup time/cost, and inter-period carryover of setup status. We further derive several valid inequalities that are based on the embedded subproblem structure. We also consider an alternative formulation (termed the Plant Location model) based on the lot-sizing perspective of the problem. Noting the resemblance of the campaign sequencing subproblem to the high multiplicity asymmetric traveling salesman problem (HMATSP), we adapt various ideas from the HMATSP to enforce the connectivity of the sequencing graph. Due to the complexity of this problem, we also explore the possibility of applying column generation technique for its solution. Various schemes of problem decomposition are considered, along with the use of dual stabilization technique to improve the convergence of the column generation procedure. We also develop heuristic methods to generate initial feasible solutions that further enhance the performance of the column generation method. A computational experimentation has been conducted on a data set that mimics real-life problem instances. It illustrates the effectiveness of using the proposed column generation method. / Ph. D.