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Master’s thesis deals with designing decision making system based on fuzzy logic principles which evaluates suppliers offers. In this Master’s thesis there were created two decision making systems based on the demands of the company. First decision making system was designed within the environment of MS Excel, the second one was designed in MATLAB software. In addition of creating the decision making system the thesis puts emphasis on the analysis of the current decision making model in the company and this section will serve as a springboard to designing a decision making system based on fuzzy logic. At the beginning of the work there are included all the necessary theoretical framework on which I based the writing of this thesis.

Master’s thesis deals with the evaluation and selection of suppliers of products and services for the company Momedica, s.r.o. using fuzzy logic. The resulting fuzzy system will serve as a decision support instrument of company.

Poor cost performance of construction projects has been a major concern for both contractors and clients. The effective management of risk is thus critical to the success of any construction project and the importance of risk management has grown as projects have become more complex and competition has increased. Contractors have traditionally used financial mark-ups to cover the risk associated with construction projects but as competition increases and margins have become tighter they can no longer rely on this strategy and must improve their ability to manage risk. Furthermore, the construction industry has witnessed significant changes particularly in procurement methods with clients allocating greater risks to contractors. Evidence shows that there is a gap between existing risk management techniques and tools, mainly built on normative statistical decision theory, and their practical application by construction contractors. The main reason behind the lack of use is that risk decision making within construction organisations is heavily based upon experience, intuition and judgement and not on mathematical models. This thesis presents a model for managing global risk factors affecting construction cost performance of construction projects. The model has been developed using behavioural decision approach, fuzzy logic technology, and Artificial Intelligence technology. The methodology adopted to conduct the research involved a thorough literature survey on risk management, informal and formal discussions with construction practitioners to assess the extent of the problem, a questionnaire survey to evaluate the importance of global risk factors and, finally, repertory grid interviews aimed at eliciting relevant knowledge. There are several approaches to categorising risks permeating construction projects. This research groups risks into three main categories, namely organisation-specific, global and Acts of God. It focuses on global risk factors because they are ill-defined, less understood by contractors and difficult to model, assess and manage although they have huge impact on cost performance. Generally, contractors, especially in developing countries, have insufficient experience and knowledge to manage them effectively. The research identified the following groups of global risk factors as having significant impact on cost performance: estimator related, project related, fraudulent practices related, competition related, construction related, economy related and political related factors. The model was tested for validity through a panel of validators (experts) and crosssectional cases studies, and the general conclusion was that it could provide valuable assistance in the management of global risk factors since it is effective, efficient, flexible and user-friendly. The findings stress the need to depart from traditional approaches and to explore new directions in order to equip contractors with effective risk management tools.

This thesis deals with the use of basic means of artificial intelligence, respectively fuzzy logic to evaluate the benefits of company's potential suppliers. There are mainly used principles of fuzzy logic in MATLAB, expert systems and analysis in MS Excel respectively. VBA development environment.

This dissertation thesis deals with problems of identifying client’s solvency in the insurance business and is drawn for the insurance companies needs. The main target of this work is a construction of methodology, which will provide managers a tool to support their decision making in cases of client solvency assessment. The basic theoretical background, an overview of the current state of the analyzed subject and the description of utilized methods are presented in the introductory part of this work. In following parts of this work is introduced a real database of insurance company’s clients, which serves as a basis to accomplish the defined goal. The source data were subject to a necessary analysis to determine the cross-correlations and variables entering the decision-making model. A large variety of traditional statistical methods, including relevant software were used to analyze the data. Decision-making model was formed with the help of artificial intelligence methods, especially fuzzy logic. The technical realization of the model was made using MATLAB software. The process of insurance company’s client solvency assessment methodology creation is described in detail and elaborated into phases. The fundamental part of the methodology, decision-making model, can be easily modified and adapted to the end user’s specific needs. The text also includes a verification and implementation of the model, an interpretation of the results, a comprehensive client solvency assessment methodology process in insurance business and the definition of contribution of this methodology to practice, theory and pedagogy.

The master's thesis deals with the topic of the use of artificial intelligence for managerial decision making in the firm. This thesis contains proposal for an application of neural networks and fuzzy logic for firm's product evaluation and recommendation for selection of competent produts.

La dangerosité des défauts électriques et notamment des défauts d’arcs série dans les installations basse tension est connue depuis longtemps et représente une problématique d’actualité. La détection et la localisation de ces défauts constituent ainsi le sujet d’étude de cette thèse. Notons également, qu’à l’heure actuelle, aucun disjoncteur pour la détection des défauts d’arcs n’est équipé de la fonction localisation d’un arc sur la ligne électrique. Plusieurs méthodes de localisation des défauts d’arcs électriques séries ont été proposées dans le travail présenté. La première méthode est basée sur les paramètres d’impédance obtenus à partir des lois de Kirchhoff et ceci sur une ligne expérimentale de 49 m de longueur. La seconde méthode utilise la modélisation de ligne pour obtenir différents vecteurs de signatures utilisés pour entrainer un réseau de neurones. La troisième méthode par transformée en ondelettes est basée sur l’identification des ondes haute fréquence qui apparaissent en présence d’un défaut d’arc série. L’autre contribution majeure de cette thèse est la mise au point d’un algorithme performant de détection de la présence d’un défaut d’arc électrique par analyse du courant de ligne. L’algorithme est conçu pour détecter de manière fiable les défauts d'arc dans les modes de fonctionnement stationnaires et transitoires des appareils ménagers puis dans des configurations complexes de masquage de charges et d'appareils perturbateurs. L’algorithme repose sur l’analyse du courant de ligne par un filtre de Kalman associé à une logique de décision. La technique mise en œuvre, portant sur un seuillage adaptatif à base de logique floue (Fuzzy Logic), entraîne une réduction significative des faux déclenchements / The dangerousness of electrical defects and in particular serial arcing ones in low-voltage installations is well known and represents a topical research issue. The detection and localization of these defects is therefore the subject of this thesis. It should also be noted that, at present time, no circuit-breaker for arc fault detection is equipped with the arc location function on the power line. Several methods for locating series arc faults have been proposed in this work. For the first method, a model based on the impedance parameters of the experimental power line (length 49 meters) based on Kirchhoff's laws was developed. The second method uses line modeling to obtain different signature vectors used to train a neural network. The third wavelet transform method is based on the identification of high frequency waves that occur in the presence of a series arc fault. The other major contribution of this thesis is the development of an efficient algorithm for detecting the presence of an electrical arc fault by the line current analysis. The algorithm is designed to reliably detect series arcing faults in stationary and transient operating modes of household appliances and then in complex load masking and with disturbance device configurations. The algorithm is based on the analysis of the line current by a Kalman filter associated with a decision logic block. The technique used based on adaptive fuzzy logic thresholding logic, allows significant reduction in false triggering