Home healthcare has been a growing sector of the economy over the last three decades with roughly 23,000 companies now doing business in the U.S. producing over $56 billion in combined annual revenue. As a highly fragmented market, profitability of individual companies depends on effective management and efficient operations. This dissertation aims at reducing costs and improving productivity for home healthcare companies. The first part of the research involves the development of a new formulation for the therapist routing and scheduling problem as a mixed integer program. Given the time horizon, a set of therapists and a group of geographically dispersed patients, the objective of the model is to minimize the total cost of providing service by assigning patients to therapists while satisfying a host of constraints concerning time windows, labor regulations and contractual agreements. This problem is NP-hard and proved to be beyond the capability of commercial solvers like CPLEX. To obtain good solutions quickly, three approaches have been developed that include two heuristics and a decomposition algorithm. The first approach is a parallel GRASP that assigns patients to multiple routes in a series of rounds. During the first round, the procedure optimizes the patient distribution among the available therapists, thus trying to reach a local optimum with respect to the combined cost of the routes. Computational results show that the parallel GRASP can reduce costs by 14.54% on average for real datasets, and works efficiently on randomly generated datasets. The second approach is a sequential GRASP that constructs one route at a time. When building a route, the procedure tracks the amount of time used by the therapists each day, giving it tight control over the treatment time distribution within a route. Computational results show that the sequential GRASP provides a cost savings of 18.09% on average for the same real datasets, but gets much better solutions with significantly less CPU for the same randomly generated datasets. The third approach is a branch and price algorithm, which is designed to find exact optima within an acceptable amount of time. By decomposing the full problem by therapist, we obtain a series of constrained shortest path problems, which, by comparison are relatively easy to solve. Computational results show that, this approach is not efficient here because: 1) convergence of Dantzig-Wolfe decomposition is not fast enough; and 2) subproblem is strongly NP-hard and cannot be solved efficiently. The last part of this research studies a simpler case in which all patients have fixed appointment times. The model takes the form of a large-scale mixed-integer program, and has different computational complexity when different features are considered. With the piece-wise linear cost structure, the problem is strongly NP-hard and not solvable with CPLEX for instances of realistic size. Subsequently, a rolling horizon algorithm, two relaxed mixed-integer models and a branch-and-price algorithm were developed. Computational results show that, both the rolling horizon algorithm and two relaxed mixed-integer models can solve the problem efficiently; the branch-and-price algorithm, however, is not practical again because the convergence of Dantzig-Wolfe decomposition is slow even when stabilization techniques are applied. / text

As systems become more interconnected, the focus of engineering design must shift to include consideration for systems of systems (SoS) e ects. As the focus shifts from singular systems to systems of systems, so too must the focus shift from performance based analysis to an evaluation method that accounts for the tendency of such large scale systems to far outlive their original operational environments and continually evolve in order to adapt to the changes. It is nearly impossible to predict the nature of these changes, therefore the rst focus of this thesis is the measurement of the exibility of the SoS and its ability to evolve and adapt. Flexibility is measured using a combination of network theory and a discrete event simulation, therefore, the second focus is the development of a simulation environment that can also measure the system's performance for baseline comparisons. The results indicate that simulated exibility is related to the performance and cost of the SoS and is worth measuring during the design process. The third focus of this thesis is to reduce the computational costs of SoS design evaluation by developing heuristics for exibility. This was done by developing a network model to correspond with the discrete event simulation and evaluating network properties using graph theory. It was shown that the network properties can correlate with simulated exibility. In such cases it was shown that the heuristics could be used in connection with an evolutionary algorithm to rapidly search the design space for good solutions. The entire methodology was demonstrated on a multi-platform maintenance planning problem in connection with the Navy Hardware Open System Technologies initiative.

Commonly used simulation models for predicting outbreaks of re-emerging infectious diseases (EIDs) take an individual-level or a population-level approach to modeling contact dynamics. These approaches are a trade-off between the ability to incorporate individual-level dynamics and computational efficiency. Agent-based network models (ABNM) use an individual-level approach by simulating the entire population and its contact structure, which increases the ability of adding detailed individual-level characteristics. However, as this method is computationally expensive, ABNMs use scaled-down versions of the full population, which are unsuitable for low prevalence diseases as the number of infected cases would become negligible during scaling-down. Compartmental models use differential equations to simulate population-level features, which is computationally inexpensive and can model full-scale populations. However, as the compartmental model framework assumes random mixing between people, it is not suitable for diseases where the underlying contact structures are a significant feature of disease epidemiology. Therefore, current methods are unsuitable for simulating diseases that have low prevalence and where the contact structures are significant. The conceptual framework for a new simulation method, Evolving Contact Network Algorithm (ECNA), was recently proposed to address the above gap. The ECNA combines the attributes of ABNM and compartmental modeling. It generates a contact network of only infected persons and their immediate contacts, and evolves the network as new persons become infected. The conceptual framework of the ECNA is promising for application to diseases with low prevalence and where contact structures are significant. This thesis develops and tests different algorithms to advance the computational capabilities of the ECNA and its flexibility to model different network settings. These features are key components that determine the feasibility of ECNA for application to disease prediction. Results indicate that the ECNA is nearly 20 times faster than ABNM when simulating a population of size 150,000 and flexible for modeling networks with two contact layers and communities. Considering uncertainties in epidemiological features and origin of future EIDs, there is a significant need for a computationally efficient method that is suitable for analyses of a range of potential EIDs at a global scale. This work holds promise towards the development of such a model.

Diploma thesis deals with four laboratory tasks in simulation environment GNS3. Designed tasks are primarily focused on comparison of IPv4 and IPv6 protocols. In the first task the subject is concerned about OSPFv2 and OSPFv3 routing protocols. Next themes are transit techniques like NAT-PT and tunneling like GRE and 6to4. The second task is focused on configuration of routing protocols like EIGRP and EIGRPv6. Next sections are concerned about DHCP and ICMP protocols within IPv4 and IPv6 protocol suits. The third task is primarily focused on security relations of protocol suite IPv6. It contains OSPFv3 authentication, access lists and Cisco stateful IOS firewall. Content of the fourth task is protocol MPLS. First part of this task is concerned about basic configuration of this protocol and second part is focused on MPLS within IPv6 environment. All tasks contain test questions and individual part task.

Employee selection research identifies and makes use of associations between individual differences, such as those measured by psychological testing, and individual differences in job performance. Artificial neural networks are computer simulations of biological nerve systems that can be used to model unspecified relationships between sets of numbers. Thirty-five neural networks were trained to estimate normalized annual revenue produced by telephone sales agents based on personality and biographic predictors using concurrent validation data (N=1085). Accuracy of the neural estimates was compared to OLS regression and a proprietary nonlinear model used by the participating company to select agents.

We demonstrate the success of recurrent neural networks in gene network inference and expression prediction using a hybrid of particle swarm optimization and differential evolution to overcome the classic obstacle of local minima in training recurrent neural networks. We also provide an improved validation framework for the evaluation of genetic network modeling systems that will result in better generalization and long-term prediction capability. Success in the modeling of gene regulation and prediction of gene expression will lead to more rapid discovery and development of therapeutic medicine, earlier diagnosis and treatment of adverse conditions, and vast advancements in life science research.

Doctor of Philosophy / Department of Electrical and Computer Engineering / Sanjoy Das / Stochastic algorithms are widely used in various modeling and optimization problems. Evolutionary algorithms are one class of population-based stochastic approaches that are inspired from Darwinian evolutionary theory. A population of candidate solutions is initialized at the first generation of the algorithm. Two variation operators, crossover and mutation, that mimic the real world evolutionary process, are applied on the population to produce new solutions from old ones. Selection based on the concept of survival of the fittest is used to preserve parent solutions for next generation. Examples of such algorithms include genetic algorithm (GA) and genetic programming (GP). Nevertheless, other stochastic algorithms may be inspired from animals’ behavior such as particle swarm optimization (PSO), which imitates the cooperation of a flock of birds. In addition, stochastic algorithms are able to address multi-objective optimization problems by using the concept of dominance. Accordingly, a set of solutions that do not dominate each other will be obtained, instead of just one best solution. This thesis proposes a multi-objective GP-PSO hybrid algorithm to recover gene regulatory network models that take environmental data as stimulus input. The algorithm infers a model based on both phenotypic and gene expression data. The proposed approach is able to simultaneously infer network structures and estimate their associated parameters, instead of doing one or the other iteratively as other algorithms need to. In addition, a non-dominated sorting approach and an adaptive histogram method based on the hypergrid strategy are adopted to address ‘convergence’ and ‘diversity’ issues in multi-objective optimization. Gene network models obtained from the proposed algorithm are compared to a synthetic network, which mimics key features of Arabidopsis flowering control system, visually and numerically. Data predicted by the model are compared to synthetic data, to verify that they are able to closely approximate the available phenotypic and gene expression data. At the end of this thesis, a novel breeding strategy, termed network assisted selection, is proposed as an extension of our hybrid approach and application of obtained models for plant breeding. Breeding simulations based on network assisted selection are compared to one common breeding strategy, marker assisted selection. The results show that NAS is better both in terms of breeding speed and final phenotypic level.

Este estudo tem como objetivo analisar os trade-offs existentes entre os custos logísticos, os incentivos fiscais baseados no ICMS e o custo da neutralização das emissões de carbono geradas nos problemas de localização de Fábricas e Centros de Distribuição. Para isso, elaborou-se um modelo de programação linear inteira mista (PLIM) em GAMS, capaz de determinar o menor custo total de um problema, através da otimização de sua função objetivo composta pelos custos fixos e variáveis dos centros de distribuição e fábricas, custos de transporte (frete de transferência e distribuição), benefícios fiscais e custos ambientais. O modelo foi elaborado de modo a possuir flexibilidade suficiente para simular os diversos cenários que se fizeram necessários durante as análises. Utilizando-se deste modelo, foram avaliados diversos cenários com base em dados reais de uma empresa de bens de consumo não duráveis. Alguns desses cenários estudados mostraram algumas distorções causadas pela existência de incentivos fiscais em alguns Estados brasileiros, mostrando como a guerra fiscal no País pode influenciar decisões estratégicas de negócio. A partir dos resultados obtidos, concluiu-se que o benefício fiscal associado ao crédito presumido de ICMS tem impacto significativo nas decisões de localização, reduzindo de forma relevante os custos totais. Já os custos ambientais, relacionados a neutralização das emissões de carbono, apesar de serem importantes nas decisões de empresas social e ambientalmente responsáveis, possuem peso econômico desprezível e não alteram o resultado da análise. Isso mostra que a política fiscal brasileira gera um aumento da emissão de poluentes na atmosfera e um aumento do desgaste e do fluxo de veículos de transporte pelas rodovias do País. / The main object of this work piece is to analyze existing trade-offs among logistic costs, tax incentives based on ICMS and carbon emission volume variations, to be able to define how these factors influence the network localization of Plants and Distribution Centers. To achieve this objective, a Mixed Integer Linear Programming model was developed in GAMS. The model is able to determine the minimum total cost for a given problem through the optimization of a specific objective function. The components of the objective function are: storage costs, transportation costs (transference and distribution freights), operational fixed costs and tax incentives. The model was designed to have enough flexibility to simulate multiple scenarios required to carry out the analysis. Several logistics configurations were examined using this model. All of the scenarios were established based on real data provided by a consumer goods industry. Nevertheless, some of the studied network configurations are distortions caused by existing tax incentives in some Brazilian states, showing how the fiscal war can influence strategic business decisions. Based on the results, one concludes that the tax benefits associated to the ICMS discounts applied in some Brazilian states actually have significant impact in the location decisions because it cuts down a relevant portion of the operational costs, whereas the carbon credits do not change the chosen network configuration, once it has shown a limited potential for financial benefit. The carbon emissions reduction is, in the other hand, an important aspect of the decisions making in social and environmental responsible companies as it can modify the image of the institution and the way it is perceived by the market.

[pt] O mercado brasileiro de combustíveis apresenta uma nova realidade com a mudança na política de preços praticados por sua principal empresa de petróleo, onde até o ano de 2016 foi caracterizado pelo monopólio devido aos preços artificiais impostos por esta de modo a controlar a inflação no país. Atualmente os preços dos produtos nas refinarias nacionais estão alinhados ao mercado internacional de commodities, viabilizando a entrada de novos competidores para atender a demanda do país. Com este cenário, surgem questões relativas a utilização do refino e níveis de preços a serem adotados no mercado interno de forma a trazer maior competitividade no mercado de forma duradoura e sustentável. Modelos de otimização são utilizados para suportar a tomada de decisão no planejamento da cadeia de downstream e definir a melhor utilização dos recursos disponíveis. Clientes e fornecedores possuem objetivos e custos diferentes, e a necessidade de integrar modelos que dialoguem entre as cadeias de abastecimento destes grupos faz-se necessária, onde os resultados da empresa são impactados de acordo com suas decisões de produção e participação no mercado. O experimento consistiu na construção uma modelagem de rede para a cadeia de distribuição de combustíveis no Brasil a partir de duas ferramentas de otimização existentes, uma delas utilizando-se SIG. Assim, esta modelagem traz uma aplicação eficaz para a empresa, pois a auxilia na quantificação de seus resultados em um cenário de competição em que a mesma se encontra inserida, considerando as singularidades do mercado e indústria no país. / [en] A recent change in the national Brazilian oil company price policy introduced a new market reality as the imposed artificial prices scheme used in order to control inflation was abandoned. Currently, refined products prices in the national territory are matched to the international commodities market, allowing the entry of new competitors to meet national demands. According to this scenario issues relating to infrastructure and a new set of prices to be adopted by the Brazilian domestic market aiming for increased competitiveness on the national market on a long-lasting and sustainable basis begin to appear. Optimization models are used to support the downstream supply chain planning decisionmaking and to ensure the best use of available production resources. Customers and suppliers have different objectives and costs, and it is necessary to integrate models which allow supply chain groups to dialogue among themselves, where the company s result are affected according to their production decisions and market share. The experiment described here consists of the building of a network modelling for the Brazilian fuel distribution chain starting from two optimization tools already available, one of them using GIS. Thus, this modelling brings an effective application to the company, as it assists in the quantification of its results in a competition scenario in which it is inserted, considering the singularities of the Brazilian market and industry.

Le terme symbiose recouvre tous types d'interactions entre espèces et peut être défini comme une association étroite d'espèces différentes vivant ensemble. De telles interactions impliquant des micro-organismes présentent un intérêt particulier pour l'agriculture, la santé, et les questions environnementales. Tous les types d'interactions entre espèces tels que le mutualisme, le commensalisme, et la compétition, sont omniprésents dans la nature et impliquent souvent le métabolisme. La libération de métabolites par des organismes dans l'environnement permet à d'autres individus de la même espèce ou de différentes espèces de les récupérer pour leur usage propre. Dans cette thèse, nous étudions comment les interactions entre espèces façonnentl'environnement. Nous examinons les questions de (i) quels sont les besoins minimaux en éléments nutritifs pour établir la croissance, et (ii) quels métabolites peuvent être échangés entre un organisme et son environnement. L'énumération de tous les ensembles minimaux stoechiométriques de précurseurs et de tous les ensembles minimaux de métabolites échangés,en utilisant des modèles complets de réseaux métaboliques, fournit un meilleur aperçu des interactions entre les espèces. Dans un environnement spatialement homogène, les métabolites qui sont libérés dans un tel environnement sont partagés par tous les individus. Le problème qui se pose alors est de savoir comment les tricheurs, les individus qui profitent des métabolites libérés sans contribuer au bien public, peuvent être exclus de la population. Ceci et d'autres configurations ont déjà été modélisées avec des approches de la théorie des jeux et de l'économie. Nous examinons comment les concepts d'ensembles minimaux de précurseurs stoechiométriques et d'ensembles minimaux de composés échangés peuvent être introduits dans ces modèles / Symbiosis, a term that brings all types of species interaction under one banner, is defined as a close association of different species living together. Species interactions that comprise microorganisms are of particular interest for agriculture, health, and environmental issues. All kinds of species interactions such as mutualism, commensalism, and competition, are omnipresent in nature and occur often at the metabolic level. Organisms release metabolites to the environment which are then taken up by other individuals of the same or of different species. In this thesis, we study how species interactions shape the environment. We examine the questions of (i) what are the minimal nutrient requirements to sustain growth, and (ii) which metabolites can be exchanged between an organism and its environment. Enumerating all minimal stoichiometric precursor sets, and all minimal sets of exchanged metabolites, using metabolic network models, provide a better insight into species interactions. In a spatially homogeneous environment, the metabolites that are released to such an environment are shared by all individuals. The problem that then arises is how cheaters, individuals that profit from the released metabolites without contributing to the public good, can be prevented from the population. This and other configurations were already modeled with approaches from game theory and economics. We examine how the concepts of minimal stoichiometric precursor sets and minimal sets of exchanged compounds can be introduced into such models