Training typically begins with a pre-existing scenario. The training exercise is performed and then an after action review is sometimes held. This “training pipeline” is repeated for each scenario that will be used that day. This approach is used routinely and often effectively, yet it has a number of aspects that can result in poor training. In particular, this process commonly has two associated events that are undesirable. First, scenarios are re-used over and over, which can reduce their effectiveness in training. Second, additional responsibility is placed on the individual training facilitator in that the trainer must now track performance improvements between scenarios. Taking both together can result in a multiplicative degradation in effectiveness. Within any simulation training exercise, a scenario definition is the starting point. While these are, unfortunately, re-used and over-used, they can, in fact, be generated from scratch each time. Typically, scenarios include the entire configuration for the simulators such as entities used, time of day, weather effects, entity starting locations and, where applicable, munitions effects. In addition, a background story (exercise briefing) is given to the trainees. The leader often then develops a mission plan that is shared with the trainee group. Given all of these issues, scientists began to explore more purposeful, targeted training. Rather than an ad-hoc creation of a simulation experience, there was an increased focus on the content of the experience and its effects on training. Previous work in scenario generation, interactive storytelling and computational approaches, while providing a good foundation, fall short on addressing the need for iv adaptive, automatic scenario generation. This dissertation addresses this need by building up a conceptual model to represent scenarios, mapping that conceptual model to a computational model, and then applying a newer procedural modeling technique, known as Functional L-systems, to create scenarios given a training objective, scenario complexity level desired, and sets of baseline and vignette scenario facets. A software package, known as PYTHAGORAS, was built and is presented that incorporates all these contributions into an actual tool for creating scenarios (both manual and automatic approaches are included). This package is then evaluated by subject matter experts in a scenario-based “Turing Test” of sorts where both system-generated scenarios and human-generated scenarios are evaluated by independent reviewers. The results are presented from various angles. Finally, a review of how such a tool can affect the training pipeline is included. In addition, a number of areas into which scenario generation can be expanded are reviewed. These focus on additional elements of both the training environment (e.g., buildings, interiors, etc.) and the training process (e.g., scenario write-ups, etc.).

Decision making under uncertainty is an important yet challenging task; a number of alternative paradigms which address this problem have been proposed. Stochastic Programming (SP) and Robust Optimization (RO) are two such modelling ap-proaches, which we consider; these are natural extensions of Mathematical Pro-gramming modelling. The process that goes from the conceptualization of an SP model to its solution and the use of the optimization results is complex in respect to its deterministic counterpart. Many factors contribute to this complexity: (i) the representation of the random behaviour of the model parameters, (ii) the interfac-ing of the decision model with the model of randomness, (iii) the difficulty in solving (very) large model instances, (iv) the requirements for result analysis and perfor-mance evaluation through simulation techniques. An overview of the software tools which support stochastic programming modelling is given, and a conceptual struc-ture and the architecture of such tools are presented. This conceptualization is pre-sented as various interacting modules, namely (i) scenario generators, (ii) model generators, (iii) solvers and (iv) performance evaluation. Reflecting this research, we have redesigned and extended an established modelling system to support modelling under uncertainty. The collective system which integrates these other-wise disparate set of model formulations within a common framework is innovative and makes the resulting system a powerful modelling tool. The introduction of sce-nario generation in the ex-ante decision model and the integration with simulation and evaluation for the purpose of ex-post analysis by the use of workflows is novel and makes a contribution to knowledge.

Developing training scenarios that induce a trainee to utilize specific skills is one of the facets of simulation-based training that requires significant effort. Simulation-based training systems have become more complex in recent years. Because of this added complexity, the amount of effort required to generate and maintain training scenarios has increased. This thesis describes an investigation into automating the scenario generation process. The Automated Scenario Generation System (ASGS) generates expected action flow as contexts in chronological order from several events and tasks with estimated time for the entire training mission. When the training objectives and conditions are defined, the ASGS will automatically generate a scenario, with some randomization to ensure no two equivalent scenarios are identical. This makes it possible to train different groups of trainees sequentially who may have the same level or training objectives without using a single scenario repeatedly. The thesis describes the prototype ASGS and the evaluation results are described and discussed. SVSTM Desktop is used as the development infrastructure for ASGS as prototype training system.

We consider the research problem of quantitative support for decision making in supply chain network design (SCND). We first identify the requirements for a comprehensive SCND as (i) a methodology to select uncertainties, (ii) a stochastic optimisation model, and (iii) an appropriate solution algorithm. We propose a process to select a manageable number of uncertainties to be included in a stochastic program for SCND. We develop a comprehensive two-stage stochastic program for SCND that includes uncertainty in demand, currency exchange rates, labour costs, productivity, supplier costs, and transport costs. Also, we consider conditional value at risk (CV@R) to explore the trade-off between risk and return. We use a scenario generator based on moment matching to represent the multivariate uncertainty. The resulting stochastic integer program is computationally challenging and we propose a novel iterative solution algorithm called adaptive scenario refinement (ASR) to process the problem. We describe the rationale underlying ASR, validate it for a set of benchmark problems, and discuss the benefits of the algorithm applied to our SCND problem. Finally, we demonstrate the benefits of the proposed model in a case study and show that multiple sources of uncertainty and risk are important to consider in the SCND. Whereas in the literature most research is on demand uncertainty, our study suggests that exchange rate uncertainty is more important for the choice of optimal supply chain strategies in international production networks. The SCND model and the use of the coherent downside risk measure in the stochastic program are innovative and novel; these and the ASR solution algorithm taken together make contributions to knowledge.

No description available.

Due to turbulence in the financial market throughout history, stress testing has become a growing part of the risk analysis performed by clearing houses. Events connected to previous crises have increased the demand for prudent risk exposure, and in this thesis we investigate regulators view on how CCPs should construct risk scenarios to meet best practice for stress testing their members’ composite portfolios. A method based on multivariate t-distributions and copula-transformations applied to historical time series data, is proposed for constructing an independent scenario generator which should be used as a compliment to other, more knowledge-based methods. The method was implemented in Matlab to test the theory in practice, and experiments were setup for pure stock portfolios as well as for derivative based portfolios. Backtests were then carried out to validate the underlying theory on historical data spanning 25 years in total. Results show that the method proposed in this thesis indeed has the potential to be a useful approach for creating stress scenarios. Its ability to render specific levels of plausibility seems to show a sufficient level of consistency with real life data, and further research is thereby justified.

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.

The Prime Ministry of Undersecretariat of Treasury maintaining the financial administration of Republic of Turkey has several tasks to handle one of which is to manage the government&rsquo / s debt in a way that minimizes the cost regarding risk. Choosing the right instrument and maturity composition that has the least cost and risk is the debt management problem to be dealt with and is affected by many stochastic factors. The objective of this thesis is the optimization of the debt management problem of the Turkish Government via a stochastic simulation framework under the constraints of changes in portfolio positions. Value-at-Risk of the optimal portfolio is calculated to measure market risk. Macroeconomic variables in the optimization problem are modeled with econometric models like autoregressive processes (AR), autoregressive integrated moving average processes (ARIMA) and generalized autoregressive conditionally heteroscedastic (GARCH) processes. The simulation horizon is 2005-2015. Debt portfolio is optimized at 2006 and 2015 where the representative scenarios for the optimization are found by clustering the previously generated 25,000 scenarios into 30 groups at each stage.

This dissertation primarily proposes data-driven methods to handle uncertainty in problems related to Enterprise-wide Optimization (EWO). Datadriven methods are characterized by the direct use of data (historical and/or forecast) in the construction of models for the uncertain parameters that naturally arise from real-world applications. Such uncertainty models are then incorporated into the optimization model describing the operations of an enterprise. Before addressing uncertainty in EWO problems, Chapter 2 deals with the integration of deterministic planning and scheduling operations of a network of batch plants. The main contributions of this chapter include the modeling of sequence-dependent changeovers across time periods for a unitspecific general precedence scheduling formulation, the hybrid decomposition scheme using Bilevel and Temporal Lagrangean Decomposition approaches, and the solution of subproblems in parallel. Chapters 3 to 6 propose different data analytics techniques to account for stochasticity in EWO problems. Chapter 3 deals with scenario generation via statistical property matching in the context of stochastic programming. A distribution matching problem is proposed that addresses the under-specification shortcoming of the originally proposed moment matching method. Chapter 4 deals with data-driven individual and joint chance constraints with right-hand side uncertainty. The distributions are estimated with kernel smoothing and are considered to be in a confidence set, which is also considered to contain the true, unknown distributions. The chapter proposes the calculation of the size of the confidence set based on the standard errors estimated from the smoothing process. Chapter 5 proposes the use of quantile regression to model production variability in the context of Sales & Operations Planning. The approach relies on available historical data of actual vs. planned production rates from which the deviation from plan is defined and considered a random variable. Chapter 6 addresses the combined optimal procurement contract selection and pricing problems. Different price-response models, linear and nonlinear, are considered in the latter problem. Results show that setting selling prices in the presence of uncertainty leads to the use of different purchasing contracts.

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.