This study develops a systems integration project scheduling model which identifies the assignment of activity responsibilities that minimizes expected project implementation cost, considering the project risk. Assignment of resources to the individual jobs comprising the project is a persistent problem in project management. Mostly, skilled labor is an essential resource and both the time and the cost incurred to perform a job depend on the resource to which job is assigned. A systems integration project includes implementation issues in the areas of shipping, installation, and commissioning. Implementation problems lead to project delays, increased costs, and decreased performance, leading to customer dissatisfaction with the systems integrator. Activities can be performed in one of three ways: by the integrator, by the customer, or jointly between the integrator and customer. In this study we select the performer (mode) of each activity comprising the project network while taking into consideration the varying cost, duration and extreme event probability of each activity among different modes-integrator, joint work and customer. Use of the model will permit customers and integrators to mutually agree on an appropriate assignment of responsibilities in the contract. Systems integrators can also use the model to improve their implementation services offerings. An experimental design and a Monte-Carlo simulation study were conducted to see the effects of the parameters of the problem on the selection of modes.

A percepção crescente da sociedade em relação aos perigos inerentes às instalações industriais que manipulam grandes inventários de substâncias perigosas faz com que a ferramenta análise quantitativa de risco ganhe importância na complexa discussão sobre a viabilidade destes empreendimentos, no intuito de promover a ocupação adequada do solo na área urbana e prevenir a ocorrência do chamado acidente maior. Contudo, para se chegar à expressão de risco de uma determinada instalação industrial deve-se aplicar um conjunto de técnicas e de modelos matemáticos, entre os quais estão os modelos de dispersão atmosférica, usados para se estimar a área afetada na vizinhança da mesma por liberações acidentais que levam à formação de nuvens de substâncias químicas na atmosfera. Em decorrência da complexidade inerente ao próprio processo de dispersão atmosférica, especialmente no que tange aos denominados gases densos, existe uma diversidade de modelos que podem ser aplicados no escopo da análise de risco, o que leva a seus usuários, naturalmente, ao questionamento sobre a suscetibilidade dos resultados finais ao tipo de modelagem adotada. Neste sentido, este trabalho estuda o processo de dispersão atmosférica de nuvens densas formadas em liberações acidentais, identificando as principais possibilidades de modelagem deste processo e, ao final, apresenta um estudo de caso demonstrando que diferentes modelagens desta dispersão, comumente empregadas em análise de risco de instalações industriais, podem produzir variações na estimativa do risco de uma mesma instalação e, portanto, influenciar as decisões baseadas em risco. / The concern of the society about the risks posed by activities that deal with hazardous substances has increased in an environment strongly industrialized and with high population density in view of the inherent potential hazards of them as well as the impact of recent accidental episodes, even though their benefits provided. In this context the quantitative risk analysis is presented as an essential tool to assess the risk of these activities and compose a complex discussion about its feasibility. Some of these accident scenarios may involve the formation of a hazardous product cloud and its subsequent air dispersion in the off-site region when an accidental released take place and one should apply the so-called atmospheric dispersion models for estimating the consequences of the releases. Due to the complexity involved in this atmospheric dispersion process, there is a wide variety of mathematical models that can be applied for estimating the offsite consequences of the accidental releases leading, naturally, to one wonder whether the final risk expression of a facility is susceptible to these differences. Often in the world of industrial use of hazardous materials, toxic or flammable there is a possibility that these accidental releases produce clouds that are denser than air, a situation that demands even more attention in terms of risk aspects involved. Then, this dissertation studies the process of atmospheric dispersion of heavier-than-air clouds produced after an accidental release, identifying the main ways of modelling the process and presents a case study comparing different dispersion models that demonstrates that the final expression of risk of a typical installation can be different when it is used different dispersion model in the process.

Cette thèse porte sur la modélisation, la prévision et la couverture du risque de contagion financière. Après une présentation générale des fondements théoriques et des mécanismes de propagation relatifs à la contagion financière, nous introduisons une modélisation fondée sur les modèles de cointégration non linéaire et de causalité non linéaire dans lesquels, les variables et le terme d’erreur du modèle à correction d’erreur obéissent à la dynamique de processus auto-régressifs à changement de régime de type TAR et M-TAR pour capter l’effet de contagion. Une extension de cette modélisation au cadre de prévision probabiliste conditionnelle a été faite par la suite à travers les réseaux de croyance Bayésienne pour renforcer le pouvoir prédictif. Ensuite, nous montrons comment une institution financière peut couvrir son portefeuille contre ce type de risque par de nouvelles approches. Nous proposons pour cela, une stratégie de couverture purement statique dans une perspective règlementaire à l’aide de modèles génératifs de type Vines-copula, une stratégie de couverture semi-statique fondée sur la budgétisation des risques et une stratégie de couverture dynamique à partir des processus de diffusion à sauts mutualisés. Ces nouvelles modélisations sont testées empiriquement sur un ensemble d’indices boursiers. / This Ph.D thesis focuses on modeling, forecasting and hedging financial contagion. After an overview of the theoretical foundations and spread mechanism relating to financial contagion, we introduce modeling based on nonlinear cointegration and non-linear causality models in which the variables and the error term in the correction model error obey at the dynamics of autoregressive regime change process of type TAR and M-TAR to catch the contagion effect. An extension of this model to conditional probabilistic forecasting framework was done through Bayesian belief networks, to enhance the predictive power. Then we show how a financial institution can hedge its portfolio against this risk by new specifications. Therefore, we offer a purely static hedging strategy in a regulatory perspective using generative models Vines-copula, a semi-static hedging strategy based on risk budgeting and dynamic hedging strategy based on mutually exciting jumps diffusion process. These new models are tested empirically on set of market indices.

学位プログラム名: 京都大学大学院思修館 / 京都大学 / 新制・課程博士 / 博士(総合学術) / 甲第24056号 / 総総博第25号 / 新制||総総||4(附属図書館) / 京都大学大学院総合生存学館総合生存学専攻 / (主査)教授 山敷 庸亮, 教授 寶 馨, 教授 池田 裕一, 佐藤 達彦 / 学位規則第4条第1項該当 / Doctor of Philosophy / Kyoto University / DFAM

Les maladies émergentes à transmission vectorielle sont une préoccupation croissante et particulièrement lorsqu’elles affectent les chevaux, une population spécifiquement à risque vis-à-vis de la propagation de maladies. En effet, les chevaux voyagent fréquemment et, malgré l’impact sanitaire et économique des maladies équines, les règlementations sanitaires et les principes de biosécurité et de traçabilité censés assurer la sécurité des mouvements d'équidés ne sont pas toujours en place. Notre travail propose d'améliorer la surveillance des maladies à transmission vectorielle chez les chevaux en utilisant différentes méthodes pour estimer la probabilité d'émergence d'une maladie. Tout d'abord, nous avons développé un modèle quantitatif et spatio-temporel combinant différentes probabilités pour estimer les risques d'introduction de la peste équine et de l’encéphalose équine. Ces combinaisons permettent d’obtenir une image plus détaillée du risque posé par ces agents pathogènes. Nous avons ensuite évalué des systèmes de surveillance syndromique par deux approches méthodologiques: l'approche classique avec un seuil d'alarme basé sur un multiple de l'erreur standard de prédiction, et l'approche bayésienne basée sur le rapport de vraisemblance. Nous avons travaillé ici principalement sur la détection précoce du virus West Nile en utilisant les symptômes nerveux des chevaux. Les deux approches ont fourni des résultats prometteurs, mais l’approche bayésienne était particulièrement intéressante pour obtenir un résultat quantitatif et pour combiner différentes informations épidémiologiques. Pour finir, l'approche bayésienne a été utilisée pour combiner quantitativement différentes sources d'estimation du risque : surveillance syndromique multivariée, et combinaison de la surveillance syndromique avec les résultats d’analyses de risques. Ces combinaisons ont données des résultats prometteurs. Ce travail, basé sur des estimations de risque, contribue à améliorer la surveillance des maladies à transmission vectorielle chez les chevaux et facilite la prise de décision. Les principales perspectives de ce travail sont d'améliorer la collecte et le partage de données, de mettre en oeuvre une évaluation complète des performances des systèmes de surveillance multivariés, et de favoriser l'adoption de ce genre d’approche par les décideurs en utilisant une interface conviviale et en mettant en place un transfert de connaissance. / Emerging vector-borne diseases are a growing concern, especially for horse populations, which are at particular risk for disease spread. In general, horses travel widely and frequently and, despite the health and economic impacts of equine diseases, effective health regulations and biosecurity systems to ensure safe equine movements are not always in place. The present work proposes to improve the surveillance of vector-borne diseases in horses through the use of different approaches that assess the probability of occurrence of a newly introduced epidemic. First, we developed a spatiotemporal quantitative model which combined various probabilities in order to estimate the risk of introduction of African horse sickness and equine encephalosis. Such combinations of risk provided more a detailed picture of the true risk posed by these pathogens. Second, we assessed syndromic surveillance systems using two approaches: a classical approach with the alarm threshold based on the standard error of prediction, and a Bayesian approach based on a likelihood ratio. We focused particularly on the early detection of West Nile virus using reports of nervous symptoms in horses. Both approaches provided interesting results but Bayes’ rule was especially useful as it provided a quantitative output and was able to combine different epidemiological information. Finally, a Bayesian approach was also used to quantitatively combine various sources of risk estimation in a multivariate syndromic surveillance system, as well as a combination of quantitative risk assessment with syndromic surveillance (applied to West Nile virus and equine encephalosis, respectively). Combining evidence provided promising results. This work, based on risk estimations, strengthens the surveillance of VBDs in horses and can support public health decision making. It also, however, highlights the need to improve data collection and data sharing, to implement full performance assessments of complex surveillance systems, and to use effective communication and training to promote the adoption of these approaches.