Disasters impact different communities differently due to pre-existing vulnerabilities and inequalities, which diversify amounts and types of public service needs. Understanding the varying needs of communities that rely on government services helps decision-makers allocate limited resources properly during crises to maintain effective, efficient, and equitable service provision across the region. This dissertation includes three independent studies which commonly investigate how service operations can be successfully managed to maintain the operational performance goals of public organizations during slow-onset disasters. The first study focuses on the volatility in the service needs of citizens from a public system during a long-term disaster. The study proposes a time series approach for predicting demand volatility patterns to manage service productivity. This chapter explores the longitudinal impacts of long-term disasters for better service performance management since the timely and accurate prediction of deviations from the expected service demand is vital for utilizing limited resources. The study further discusses the differential impacts of such disasters across locations of socio-economically diverse populations to emphasize the need to consider the diverse needs of people for efficient and effective service provision. The second study builds upon the discussions in the first study and discusses static and dynamic risk factors of slow-onset disasters to reveal how these factors diversify the service needs of communities and impact the corresponding service response performance of public systems during the disaster. The study performs time series analyses to test the impact of capacity adjustments and dynamic disaster risk features on service performance, considering service response time as the performance indicator. The third study focuses on efficient and equitable capacity management and prioritization strategies of an information technology-based public system that experiences significant changes in service demand during disasters. The study presents a mathematical model quantifying the relative service efficiencies associated with service requests from an input-output-based standpoint to uncover the inefficiencies in response performance to different service categories. The paper discusses the opportunities for managing service efficiency and equity within and between service departments by rearranging available capacities and prioritization strategies during emergencies. / Doctor of Philosophy / Disasters impact different communities differently due to pre-existing vulnerabilities and inequalities, which diversify amounts and types of public service needs. Understanding the varying needs of communities that rely on government services helps decision-makers allocate limited resources properly during crises to maintain effective, efficient, and equitable service provision across the region. The three independent studies of the dissertation commonly investigate how service operations can be successfully managed to maintain the operational performance goals of public organizations during slow-onset disasters (e.g., climate change, drought, pandemic). The first study focuses on the variability in the service needs of citizens from a public system during a long-term disaster. The study explores the longitudinal impacts of disasters for better service performance management since the timely and accurate prediction of demand variability is important for resource management. The study further discusses the different impacts of such disasters across locations of socio-economically diverse populations to emphasize the need to consider the diverse needs of people for efficient and effective service provision. The second study discusses disaster risk factors of slow-onset disasters to reveal how these factors affect the service needs of communities and impact the corresponding service response performance of public systems. The study tests the impact of capacity adjustments and disaster risk factors on service performance. The third study focuses on efficient and equitable capacity management and prioritization strategies of a public system that experiences significant changes in service demand during disasters. The study quantifies the relative service efficiencies associated with service requests to uncover the inefficiencies in response performance to different service categories. The paper discusses the opportunities for managing service efficiency and equity within and between service departments.

We propose a novel analytic approach for the comparative statics analysis of operations management problems on the capacity investment decision and the influenza (flu) vaccine composition decision. Our approach involves exploiting the properties of the underlying mathematical models, and linking those properties to the concept of stochastic orders relationship. The use of stochastic orders allows us to establish our main results without restriction to a specific distribution. A major strength of our approach is that it is "scalable," i.e., it applies to capacity investment decision problem with any number of non-independent (i.e., demand or resource sharing) products and resources, and to the influenza vaccine composition problem with any number of candidate strains, without a corresponding increase in computational effort. This is unlike the current approaches commonly used in the operations management literature, which typically involve a parametric analysis followed by the use of the implicit function theorem. Providing a rigorous framework for comparative statics analysis, which can be applied to other problems that are not amenable to traditional parametric analysis, is our main contribution. We demonstrate this approach on two problems: (1) Capacity investment decision, and (2) influenza vaccine composition decision. A comparative statics analysis is integral to the study of these problems, as it allows answers to important questions such as, "does the firm acquire more or less of the different resources available as demand uncertainty increases? does the firm benefit from an increase in demand uncertainty? how does the vaccine composition change as the yield uncertainty increases?" Using our proposed approach, we establish comparative statics results on how the newsvendor's expected profit and optimal capacity decision change with demand risk and demand dependence in multi-product multi-resource newsvendor networks; and how the societal vaccination benefit, the manufacturer's profit, and the vaccine output change with the risk of random yield of strains. / Ph. D.

This research is motivated by identified gaps in contemporary planning practices and production processes within firms. Relying solely on experiential knowledge has proven limiting, necessitating a more systematic approach. Previous instances of data anomalies, particularly ongoing challenges in achieving satisfactory delivery reliability, have underlined the need for deeper insights into underlying patterns. The objectives of this study are: • To identify and analyze specific obstacles and challenges affecting load balance and delivery security in Borl.nge's production system. • To explore various methods or strategies aimed at enhancing the process of generating reliable capacity forecasting methods. Both primary and secondary research methods were employed. Primary methods included interviews and the development of a forecast model, while secondary studies encompassed the latest research in the field. The thesis revealed five primary factors hindering capacity attainment: 1. WIP(work in progress)/ slabs material shortages disrupt production flow and escalate costs due to the need for external sourcing of slabs. 2. Transport issues, including incorrect internal deliveries and the weather conditions, pose challenges. 3. Personnel shortages hinder the efficient utilization of production capacity. 4. Machine breakdowns result in production interruptions, leading to capacity loss and inefficiency. 5. Inventory problems, such as insufficient capacity and poor management, impede smooth production operations. Additionally, the second objective was addressed by implementing exponential smoothing for capacity planning forecasts. By updating forecasts every 13 weeks, this study improves the production forecast.

Este trabalho apresenta um método empírico para previsão de sobrecargas transientes em sistemas computacionais por meio de modelagem dinâmica. A técnica, baseada em aproximações lineares e invariantes no tempo, tem como objetivo identificar a capacidade de um sistema computacional absorver variações na carga de trabalho. Experimentalmente, a identificação dessa capacidade do sistema pode ser feita por meio de técnicas de avaliação de desempenho, em que a abordagem prevalente é a estimação da capacidade estática em regime estacionário de operação, observando-se o desempenho sob demanda constante. Entretanto, essa avaliação não considera o regime transiente do sistema, i.e durante o período de restabelecimento ao regime estacionário após uma perturbação, e durante o qual, o esforço exigido pode ser bastante diverso, e potencialmente acima daquele apurado sob condições de regime estacionário. A proposta deste trabalho é a formulação de uma metodologia para avaliação de desempenho em regime transiente em sistemas computacionais sob carga de trabalho variável e que forneça informação para o dimensionamento de recursos e políticas de controle de admissão que evitem sobrecargas por efeitos transitórios. A metodologia baseia-se na parametrização de um modelo dinâmico a partir de ensaios experimentais, considerando perturbações bruscas e de longa duração, e os resultados são avaliados por comparação das predições do modelo em relação aos objetivos por simulação ou aferição. / This research work introduces an empirical method for the prediction of transient overloads in computer systems by means of dynamical modeling. The technique, based on linear time-invariant approximations, aims at identifying the computer systems capacity in absorbing variations on the workload. Experimentally, this capacity identification can be carried out from performance evaluation methods, whose prevalent approach is the estimation of the static capacity under stationary operational regime, by observing the performance under constant demand. Nevertheless, this kind of evaluation does not take into account the systems transient regime, i.e. the period during of the restablishment to the stationary regime after the perturbation, and within which, the effort required from the systems may be diverse and potentially superior to that measured under the stationary condition. This work proposes the formulation of a methodology for performance evaluation in transient regime of computer systems submitted to variable workloads, aimed at providing information for dimensioning or resources and design of admission control policies capable of avoiding overloads due to transitory effects. The methodology relies on the parametrization of a dynamical model obtained from experimental procedures, considering abrupt, long-lasting distrubances, and the results are evaluated through comparison of the model prediction with the simulated system.

In this dissertation we study several non-convex and stochastic optimization problems. The common theme is the use of mixed-integer programming (MIP) techniques including valid inequalities and reformulation to solve these problems. We first study a strategic capacity planning model which captures the trade-off between the incentive to delay capacity installation to wait for improved technology and the need for some capacity to be installed to meet current demands. This problem is naturally formulated as a MIP with a bilinear objective. We develop several linear MIP formulations, along with classes of strong valid inequalities. We also present a specialized branch-and-cut algorithm to solve a compact concave formulation. Computational results indicate that these formulations can be used to solve large-scale instances. We next study methods for optimization with joint probabilistic constraints. These problems are challenging because evaluating solution feasibility requires multidimensional integration and the feasible region is not convex. We propose and analyze a Monte Carlo sampling scheme to simplify the probabilistic structure of such problems. Computational tests of the approach indicate that it can yield good feasible solutions and reasonable bounds on their quality. Next, we study a MIP formulation of the non-convex sample approximation problem. We obtain two strengthened formulations. As a byproduct of this analysis, we obtain new results for the previously studied mixing set, subject to an additional knapsack inequality. Computational results indicate that large-scale instances can be solved using the strengthened formulations. Finally, we study optimization problems with stochastic dominance constraints. A stochastic dominance constraint states that a random outcome which depends on the decision variables should stochastically dominate a given random variable. We present new formulations for both first and second order stochastic dominance which are significantly more compact than existing formulations. Computational tests illustrate the benefits of the new formulations.

We consider two classes of stochastic programming models which are motivated by two applications related to the field of aviation. The first problem we consider is the network capacity planning problem, which arises in capacity planning of systems with network structures, such as transportation terminals, roadways and telecommunication networks. We study this problem in the context of airport terminal capacity planning. In this problem, the objective is to determine the optimal design and expansion capacities for different areas of the terminal in the presence of uncertainty in future demand levels and expansion costs, such that overall passenger delay is minimized. We model this problem as a nonlinear multistage stochastic integer program with a multicommodity network flow structure. The formulation requires the use of time functions for maximum delays in passageways and processing stations, for which we derive approximations that account for the transient behavior of flow. The deterministic equivalent of the developed model is solved via a branch and bound procedure, in which a bounding heuristic is used at the nodes of the branch and bound tree to obtain integer solutions. In the second study, we consider the project portfolio optimization problem. This problem falls in the class of stochastic programs in which times of uncertainty realizations are dependent on the decisions made. The project portfolio optimization problem deals with the selection of research and development (R&D) projects and determination of optimal resource allocations for the current planning period such that the expected total discounted return or a function of this expectation for all projects over an infinite time horizon is maximized, given the uncertainties and resource limitations over a planning horizon. Accounting for endogeneity in some parameters, we propose efficient modeling and solution approaches for the resulting multistage stochastic integer programming model. We first develop a formulation that is amenable to scenario decomposition, and is applicable to the general class of stochastic problems with endogenous uncertainty. We then demonstrate the use of the sample average approximation method in solving large scale problems of this class, where the sample problems are solved through Lagrangian relaxation and lower bounding heuristics.

De nos jours, les systèmes distribués sont caractérisés par une complexité croissante de l'architecture, des fonctionnalités et de la charge soumise. Cette complexité induit souvent une perte de la qualité de service offerte, ou une saturation des ressources, voire même l'indisponibilité des services en ligne, en particulier lorsque la charge est importante. Afin d'éviter les désagrèments causés par d'importantes charges et remplir le niveau attendu de la qualité de service, les systèmes nécessitent une auto-gestion, en optimisant par exemple un tier ou en le renforçant à travers la réplication. Cette propriété autonome requiert une modélisation des performances de ces systèmes. Visant cet objectif, nous développons un framework basé sur une méthodologie théorique et expérimentale d'identification automatique de modèle et de dimensionnement, fournissant en premier un modèle de réseau de file d'attente pour un système distribué. Ensuite, ce Modèle est utilisé au sein de notre framwork pour dimensionner le système à travers une analyse ou une simulation du réseau de file d'attente. / Modern distributed systems are characterized by a growing complexity of their architecture, functionalities and workload. This complexity, and in particular significant workloads, often lead to quality of service loss, saturation and sometimes unavailability of on-line services. To avoid troubles caused by important workloads and fulfill a given level of quality of service (such as response time), systems need to self-manage, for instance by tuning or strengthening one tier through replication. This autonomic feature requires performance modelling of systems. In this objective, we developed a framework based on a theoretical and experimental approach for automatic identification process and sizing . This framework provid a queuing model for a distributed system. Then, this model is used in our Framwork to size the system through an analysis or simulation.

Made available in DSpace on 2016-06-02T19:51:59Z (GMT). No. of bitstreams: 1 5069.pdf: 2520919 bytes, checksum: b9113b4009fc58c6de6cb820cac2ad09 (MD5) Previous issue date: 2013-03-11 / It is already known that the productive environment is in constant changing, with new technologies, new systems and theories coming up every day, providing other forms to manage production to comply with inconstant demands and a competitive market. In this context, the capacity planning has an important role for a company, managing resources in a structured way with accurate information, ensuring that market requirements will be fulfilled in terms of delivery, cost and quality. The Critical Chain Method is shown as an alternative to change the way that manufacturing resources has been managed, prioritizing what is really important, avoiding waste of time and financial resources. Therefore, the objective of this work was to elaborate a model of discrete-event simulation to evaluate and compare the use of the principles of critical chain, against the traditional capacity planning method used in a foundry. For this assessment were used performance indicators as accomplishment deadlines, work in process, average lead time, etc., where these after being compared, have shown the proposed method could provide positive results to the environment studied. / É de conhecimento que o ambiente produtivo está em constante mudança, com novas tecnologias, sistemas e teorias surgindo a cada dia, proporcionando novas maneiras para gerenciar a produção e atender a um mercado concorrido e com demandas inconstantes. Nesse contexto, o planejamento da capacidade tem papel fundamental para uma empresa, gerenciando os recursos de maneira estruturada com informações precisas e garantindo o atendimento às necessidades do mercado, em termos de prazos de entrega, custo e qualidade. O Método da Corrente Crítica se mostra como uma alternativa para modificar a maneira de gerenciar os recursos fabris, priorizando o que é realmente importante e evitando-se o desperdício de tempo, e consequentemente de recursos financeiros. Logo, o objetivo deste trabalho foi elaborar um modelo de simulação de eventos discretos para avaliar e comparar a utilização dos princípios da corrente crítica, frente ao método tradicional de planejamento da capacidade utilizado em uma fundição de grande porte. Para esta avaliação foram utilizados indicadores de desempenho como atendimento aos prazos, trabalho em processo, lead time médio, etc., onde estes, após serem comparados, mostraram que o método proposto pode trazer bons resultados ao ambiente estudado.

Este trabalho apresenta um método empírico para previsão de sobrecargas transientes em sistemas computacionais por meio de modelagem dinâmica. A técnica, baseada em aproximações lineares e invariantes no tempo, tem como objetivo identificar a capacidade de um sistema computacional absorver variações na carga de trabalho. Experimentalmente, a identificação dessa capacidade do sistema pode ser feita por meio de técnicas de avaliação de desempenho, em que a abordagem prevalente é a estimação da capacidade estática em regime estacionário de operação, observando-se o desempenho sob demanda constante. Entretanto, essa avaliação não considera o regime transiente do sistema, i.e durante o período de restabelecimento ao regime estacionário após uma perturbação, e durante o qual, o esforço exigido pode ser bastante diverso, e potencialmente acima daquele apurado sob condições de regime estacionário. A proposta deste trabalho é a formulação de uma metodologia para avaliação de desempenho em regime transiente em sistemas computacionais sob carga de trabalho variável e que forneça informação para o dimensionamento de recursos e políticas de controle de admissão que evitem sobrecargas por efeitos transitórios. A metodologia baseia-se na parametrização de um modelo dinâmico a partir de ensaios experimentais, considerando perturbações bruscas e de longa duração, e os resultados são avaliados por comparação das predições do modelo em relação aos objetivos por simulação ou aferição. / This research work introduces an empirical method for the prediction of transient overloads in computer systems by means of dynamical modeling. The technique, based on linear time-invariant approximations, aims at identifying the computer systems capacity in absorbing variations on the workload. Experimentally, this capacity identification can be carried out from performance evaluation methods, whose prevalent approach is the estimation of the static capacity under stationary operational regime, by observing the performance under constant demand. Nevertheless, this kind of evaluation does not take into account the systems transient regime, i.e. the period during of the restablishment to the stationary regime after the perturbation, and within which, the effort required from the systems may be diverse and potentially superior to that measured under the stationary condition. This work proposes the formulation of a methodology for performance evaluation in transient regime of computer systems submitted to variable workloads, aimed at providing information for dimensioning or resources and design of admission control policies capable of avoiding overloads due to transitory effects. The methodology relies on the parametrization of a dynamical model obtained from experimental procedures, considering abrupt, long-lasting distrubances, and the results are evaluated through comparison of the model prediction with the simulated system.

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation.