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This master's thesis focuses on the knowledge of risk management. The core of this issue contains risk management process, risk identification, monitoring and risk analysis. The text in the first part explains stages of identifying the risks and the methodology used. The aim was to create, after a thorough analysis, the detailed design, then proceed to implementing individual parts of the system and to create a functional system for the support of identification of risks in IT projects. The system was implemented in PHP programming language, working with the MySQL database system on the database level. In the end there is an assessment and discussion of possible expansion.

Pandemic outbreaks are unpredictable as to their virus strain, transmissibility, and impact on our quality of life. Hence, the decision support models for mitigation of pandemic outbreaks must be user-friendly and operational, and also incorporate valid estimates of disease transmissibility and severity. This dissertation research is aimed at 1) reviewing the existing pandemic simulation models to identify their implementation gaps with regard to usability and operability, and suggesting research remedies, 2) increasing operability of simulation models by calibrating them via an epidemiological model that estimates infection probabilities using viral shedding profiles of concurrent pandemic and seasonal influenza, and 3) developing a testing strategy for the state laboratories, with their limited capacities, to improve their ability to estimate evolving transmissibility parameters. Our review of literature (Aim 1) indicates the need to continue model enhancements in critical areas including updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, and improvement of computational efficiency and accessibility. As regards the ease of calibration (Aim 2), we demonstrate that the simulation models, when driven by the infection probabilities obtained from our epidemiological model, accurately reproduce the disease transmissibility parameters. Assuming the availability of sufficient disease reporting infrastructure and strong compliance by both infected population and healthcare providers, our testing strategy (Aim 3) adequately supports characterization of real-time epidemiological parameters. Future research on this topic will be aimed at integrating the laboratory testing strategy with our modeling and simulation approach to develop dynamic mitigation strategies for pandemic outbreaks.

Syftet med uppsatsen är att analysera hur de specifika variablerna samverkar i investeringsanalysen bakom strategiska investeringar vid beslutsfattandet. Studien är inriktad mot den dominerande företagssektorn små och medelstora företag, vidare görs en jämförelse mellan yngre och mognare företag. För att analysera detta har en kvantitativ datainsamlingsmetod använts i form av en enkätundersökning. Forskningsfilosofin som är applicerad är deduktiv, för att på så sätt kunna dra generaliseringar i slutsatserna utifrån den empiriska analysen och fullfölja studiens syfte. Studien är baserad utifrån beslutsteori där hantering av risk och osäkerhet har en central roll. Inom området för beslutsfaktorer tillämpas även den situationsanpassade teorin. Intressentteorin och den neoklassiska ekonomiska teorin har även använts som bas.Resultatet uppvisar att det finns ett flertal samband mellan de specifika variablerna, SME-företagens livscykelstadier och investeringsanalysen. Vidare har markanta skillnader identifierats vid investeringsanalysen mellan de yngre och mognare företagen. I analysen illustreras detta med hjälp av statistiska analystabeller.Denna studie skiljer sig från tidigare forskning i ett flertal aspekter. Studien har identifierat ett gap mellan de praktiska tillämpningarna och teorin. Dessutom har studien ett extra brett omfång gentemot tidigare forskning. För att utöver detta även skapa en djupare insikt över företagets attityder och tillämpning av diverse metoder har undersökningen även jämfört yngre och mognare företags förhållningsätt i investeringsanalysen. Denna uppsats kan vara av värde för landets välfärd genom att öka förståelsen för SME-företagens förhållande i samband med strategiska investeringar. / The purpose of this paper is to analyse how specific variables interact the investment analysis behind the investments, in the decision-making process. This study is focused on small and medium sized enterprises where a comparison will be made between young and mature enterprises. To examine this, a quantitative data collection method has been adopted, with the use of a questionnaire survey. A deductive research philosophy has been applied for this study in order to draw general conclusions based on the empirical analysis and fulfil the purpose of this study. This study is based on decision theory where the management of risk and uncertainty play a central role. In the area of decision factors the contingency-theory is also applied. The stakeholder theory and the neoclassical economic theory have also been used as a base.The findings show that there are numerous links between the specific variables, SME business lifecycle stages and the investment analysis. Furthermore, clear differences have been identified in the investment analysis between the young and the more mature enterprises. The analysis is illustrated by means of statistical analysis tables.This research differs from previous studies in several aspects. The study has identified a gap between the practical applications and theory. Moreover, the study has an extra wide range over previous research. To go beyond and create a deeper understanding of the enterprises attitude and application of various methods in their continuous development, the study also compared younger and more mature enterprises approaches to the investment analysis. This paper can be useful for the country's welfare by increasing the understanding of SMEs relationship in the context of strategic investments.

Les principaux enjeux des supply chain d’aujourd’hui concernent l’adaptation à des environnements instables. Demand Driven Material Requirements Planning (DDMRP) est une méthode récente et prometteuse de gestion des flux qui a été conçue pour faire face aux problématiques actuelles. Le travail de recherche réalisé détaille et positionne DDMRP par rapport aux autres méthodes connues de pilotage de flux. Le but de ce travail est de challenger les principales promesses de DDMRP. Pour cela, un plan d’expériences a été réalisé sur un cas d’étude pour évaluer le comportement de MRP II, Kanban et DDMRP face à différentes sources de variabilité. Le dimensionnement des buffers DDMRP est un sujet majeur pour la méthode. Il a été traité sur un cas d’étude avec un travail d’optimisation. Toutes les contributions ont été expérimentées avec l’implémentation de DDMRP sur un cas réel. La thèse permet ainsi de valider certains atouts de DDMRP, tels que l’adaptation du système à différentes formes de variabilités, mais elle permet également de souligner des perspectives majeures de recherche sur ce sujet. / The main Supply Chain current issues concern the adaptation to unstable environments. Demand Driven Material Requirements Planning (DDMRP) is a recent and promising material management method that is designed to tackle these current issues. The research work details and classifies DDMRP compared to the other material management methods known. The goal of this work is to challenge the main DDMRP promises. This is why a design of experiments was realised on a case study in order to assess MRP II, Kanban and DDMRP behaviours with different variability sources. The DDMRP buffer sizing is a major issue. It was dealt with an optimisation work on a case study. All the contributions were experimented with a DDMRP implementation on a real case. The research work enables several DDMRP advantages to be validated, such as the system adjustment to different variability sources, however this work also allows research perspectives to be underlined.

Les analyses de sûreté de fonctionnement standards sont basées sur la représentation des événements redoutés par des arbres de défaillances, qui les décrivent à l'aide de combinaison logiques d'événements plus basiques (formules Booléennes complexes). Les analyses quantitatives se font avec l'hypothèse que les probabilités d'occurrence de ces événements basiques sont connues. Le but de cette thèse est d'étudier l'impact de l'incertitude épistémique sur les événements élémentaires, ainsi que la propagation de cette incertitude à de plus hauts niveaux. Le problème soulevé est comment calculer l'intervalle de probabilité dans lequel se trouvera l'occurrence d'un événement redouté, lorsque les événements basiques qui le décrivent ont eux-mêmes une probabilité imprécise. Lorsque l'indépendance stochastique est supposée, on se retrouve avec un problème NP-hard. Nous avons donc développé un algorithme permettant de calculer l'intervalle exact dans lequel se trouvera la probabilité d'occurrence d'un événement redouté, grâce à des techniques d'analyse par intervalles. Cet algorithme a également été étendu dans le cas où les probabilités des événements basiques évolueraient en fonction du temps. Nous avons également utilisé une approche par fonctions de croyance pour étudier le cas où l'indépendance stochastique des événements ne peut pas être démontrée : on suppose alors que les probabilités viennent de différentes sources d'information Indépendantes. Dans ce cas, les mesures de plausibilité et de nécessité d'une formule Booléenne complexe sont difficiles à calculer, néanmoins nous avons pu dégager des situations pratiques dans le cadre de leur utilisation pour les Arbres de défaillances pour lesquelles elles se prêtent aux calculs. / Standard approaches to reliability analysis relies on a probabilistic analysis of critical events based on fault tree representations. However in practice, and especially for preventive maintenance tasks, the probabilities ruling the occurrence of these events are seldom precisely known. The aim of this thesis is to study the impact of epistemic uncertainty on probabilities of elementary events such as failures over the probability of some higher level critical event. The fundamental problem addressed by the thesis is thus to compute the probability interval for a Boolean proposition representing a failure condition, given the probability intervals of atomic propositions. When the stochastic independence is assumed, we face a problem of interval analysis, which is NP-hard in general. We have provided an original algorithm that computes the output probability interval exactly, taking into account the monotonicity of the obtained function in terms of some variables so as to reduce the uncertainty. We have also considered the evolution of the probability interval with time, assuming parameters of the reliability function to be imprecisely known. Besides, taking advantage of the fact that a probability interval on a binary space can be modelled by a belief function, we have solved the same problem with a different assumption, namely information source independence. While the problem of computing the belief and plausibility of a Boolean proposition are even harder to compute, we have shown that in practical situations such as usual fault-trees, the additivity condition of probability theory is still valid, which simplifies this calculation. A prototype has been developed to compute the probability interval for a complex Boolean proposition.

In military battlefield domain, decision making plays a very important part because safety and protection depends upon the accurate decisions made by the commanders in complex situations. In military and defense applications, there is a need of such technology that helps leaders to take good decisions in the critical situations with information overload. With the help of multi-sensor information fusion, the amount of information can be reduced as well as uncertainties in the information in the decision making of identifying and tracking targets in the military area.   Information fusion refers to the process of getting information from different sources and fusing this information, to supply an enhanced decision support. Decision making is the very core and a vital part in the field of information fusion and better decisions can be obtained by understanding how situation awareness can be enhanced. Situation awareness is about understanding the elements of the situation i.e. circumstances of the surrounding environment, their relations and their future impacts, for better decision making. Efficient situation awareness can be achieved with the effective use of the sensors. Sensors play a very useful role in the multi-sensor fusion technology to collect the data about, for instance, the enemy regarding their movements across the border and finding relationships between different objects in the battlefield that helps the decision makers to enhance situation awareness.   The purpose of this thesis is to understand and analyze the critical issue of uncertainties that results information in overload in military battlefield domain and benefits of using multi-sensor information fusion technology to reduce uncertainties by comparing uncertainty management methods of Bayesian and Dempster Shafer theories to enhance decision making and situation awareness for identifying the targets in battlefield domain.

Le trafic routier est devenu de plus en plus intense. Une telle situation avec le manque de prudence des piétons constituent deux causes majeures de l’augmentation des accidents routiers. En France, 16% des accidents de la route en 2016 impliquent au moins un piéton et chaque année, environ de 800 piétons sont tués dans un accident de la circulation. De plus, la part des plus de 65 ans dans la mortalité piétonne est en hausse de 13% entre 2014 et 2016. Ainsi, par ce projet de thèse nous proposons une approche probabiliste pour inférer le type de comportement (à risque ou sécurisé) des piétons lors de la traversé de la rue. Cette approche se compose de 2 couches principales : Une couche basse, basée sur les techniques de vision par ordinateur, pour la collecte des paramètres des piétons, du trafic et des aménagements urbains et une couche haute, basée sur le Réseau Bayésien (RB), pour l’inférence du type de comportement. Plusieurs contributions et améliorations sont proposées pour la construction d’une telle approche que ce soit au niveau de la couche basse (techniques de détection et de suivi utilisées) ou au niveau de la couche haute (gestion des incertitudes des capteurs de vision et la mise en relation des paramètres hétérogènes et variées). / Road traffic has become more and more intense. Such as situation with thelack of attention of pedestrians are the two major causes of the increase in road accidents. In France, 16% of road accidents in 2016 involve at least one pedestrian and each year about 800 pedestrians are killed in a traffic accident. In addition, the proportion of pedestrian deaths having over 65 years old is up to 13% between 2014 and 2016. In fact, this thesis proposes a probabilistic approach to recognize pedestrians’ behavior (risky or secure) when crossing the street. This approach consists of two main layers: a low layer, based on computer vision techniques, for collecting pedestrian and traffic parameters and a high layer, based on the Bayesian Network, for behavior prediction. Several contributions and improvements are proposed for the construction of such an approach, whether at the level of the low layer (detection and tracking process) or at the level of the upper layer (management of the uncertainties of the vision sensors and the correlation of heterogeneous and varied parameters).

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