Байесовские сети доверия как вероятностная графическая модель для оценки экономических рисков

Realization of economical risks leads to occurrence of adverse effects which result in economic losses in the enterprise. The problem of different types of economical risks associated with the enterprise activities assessment and decision making systems’ construction on enterprise level as well as on different levels of enterprise performance comes up. In the paper I provide a state-of-art analysis of Bayesian belief networks use for economical risk assessment and decision making under uncertainty support in the framework of enterprise risk management. The areas of operational risk management and project risk management are singled out.Реализация экономических рисков приводит к возникновению нежелательных событий, которые характеризуются возможностью нанесения экономического ущерба предприятию. Стоит задача оценки различных типов экономических рисков, ассоциированных с деятельностью предприятия, и построения систем поддержки принятия решения как на уровне предприятия в целом, так и в различных областях функционирования предприятия. В статье представлено современное состояние применения аппарата байесовских сетей доверия для оценки экономического риска и поддержки принятия решений в условиях неопределенности в контексте риск-менеджмента предприятия. Выделены дисциплины управления операционными рисками и рисками проектов

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

ADVANCED RISK MANAGEMENT OF AN ARCTIC MARINE SEISMIC SURVEY OPERATION

This research is motivated by the lack of a robust risk management framework addressing the high risks in Arctic Marine Seismic Survey Operations (AMSSO), and the lack of transparent decision-making in Arctic shipping risk management globally. The literature review carried out herein reveals that the AMSSO and Arctic navigation involve significant risks caused by human elements and the unique features of this region. These known risk factors combine to constitute a ship-ice collision risk. This last represents the goal of the research investigation. With the complexity of the AMSSO system, three technical chapters are proposed to analyse and reduce the risks in the AMSSO. The first technical chapter deals with local risk analysis of the system. Herein, a Fuzzy Rule-based methodology is developed employing the probability distribution assessment in the form of belief degrees with Bayesian Network (BN) and Failure Mode and Effect Analysis (FMEA) for estimating the risk parameters of each hazard event using a computer-aided analysis. A case study of the application of the proposed risk model – Fuzzy Rule-based Bayesian Network (FRBN) –, in the Greenland, Iceland and Norwegian Seas (GNIS) AMSSO is carried out to identify the most critical hazard event in the prospect oil field. The second technical chapter deals with the global safety performance of the Ship-Ice Collision model dovetailing the Evidential Reasoning (ER) technique and Analytic Hierarchy Process (AHP) with the FRBN. A trial application of the global safety performance of the Ship-Ice Collision case in a prospect oil field is carried out to determine the safety level of AMSSO, measured against a developed benchmark risk. The outcome of the investigation reveals the Risk Influence Factor (RIF) of each hazard event in AMSSO. Since the risk level is far above the tolerable region of the developed benchmark risk, several Risk Control Options (RCOs) are investigated in the last technical chapter to reduce and control the critical risks. This technical chapter finalises the risk management framework developed in this research. In a trial application of reducing a critical risk in AMSSO, AHP-TOPSIS is utilised to find a balance between cost and benefit in selecting the most appropriate RCO at the heart of several RCOs and their associated criteria. The novelty of this research lies in the fact that it tackles the major concerns in risk analysis (concerns such as dynamic event risk analysis, hazard data uncertainties, and hazard event dependencies) of a complex system. More also, it adopts a hybrid methodology that offers a non-monotonic utility output to select the most appropriate RCO amongst several RCOs and conflicting criteria, to reduce the critical risks in AMSSO, in an economically viable strategy

Integrative risk-based assessment modelling of safety-critical marine and offshore applications

This research has first reviewed the current status and future aspects of marine and offshore safety assessment. The major problems identified in marine and offshore safety assessment in this research are associated with inappropriate treatment of uncertainty in data and human error issues during the modelling process. Following the identification of the research needs, this thesis has developed several analytical models for the safety assessment of marine and offshore systems/units. Such models can be effectively integrated into a risk-based framework using the marine formal safety assessment and offshore safety case concepts. Bayesian network (BN) and fuzzy logic (FL) approaches applicable to marine and offshore safety assessment have been proposed for systematically and effectively addressing uncertainty due to randomness and vagueness in data respectively. BN test cases for both a ship evacuation process and a collision scenario between the shuttle tanker and Floating, Production, Storage and Offloading unit (FPSO) have been produced within a cause-effect domain in which Bayes' theorem is the focal mechanism of inference processing. The proposed FL model incorporating fuzzy set theory and an evidential reasoning synthesis has been demonstrated on the FPSO-shuttle tanker collision scenario. The FL and BN models have been combined via mass assignment theory into a fuzzy-Bayesian network (FBN) in which the advantages of both are incorporated. This FBN model has then been demonstrated by addressing human error issues in a ship evacuation study using performance-shaping factors. It is concluded that the developed FL, BN and FBN models provide a flexible and transparent way of improving safety knowledge, assessments and practices in the marine and offshore applications. The outcomes have the potential to facilitate the decision-making process in a risk-based framework. Finally, the results of the research are summarised and areas where further research is required to improve the developed methodologies are outline

Bayesian conditioning in possibility theory

International audienceIn this paper, possibility measures are viewed as upper bounds of ill-known probabilities, since a possibility distribution is a faithful encoding of a set of lower bounds of probabilities bearing on a nested collection of subsets. Two kinds of conditioning can be envisaged in this framework, namely revision and focusing. On the one hand, revision by a sure event corresponds to adding an extra constraint enforcing that this event is impossible. On the other hand, focusing amounts to a sensitivity analysis on the conditioned probability measures (induced by the lower bound constraints). When focusing on a particular situation, the generic knowledge encoded by the probability bounds is applied to this situation, without aiming at modifying the generic knowledge. It contrasts with revision where the generic knowledge is modified by the new constraint. This paper proves that focusing applied to a possibility measure yields a possibility measure again, which means that the conditioning of a family of probabilities, induced by lower bounds bearing on probabilities of nested events, can be faithfully handled on the possibility representation itself. Relationships with similar results in the belief function setting are pointed out. Lastly the application of possibilistic focusing to exception-tolerant inference is suggested