A methodology for the selection of new technologies in the aviation industry

The purpose of this report is to present a technology selection methodology to quantify both tangible and intangible benefits of certain technology alternatives within a fuzzy environment. Specifically, it describes an application of the theory of fuzzy sets to hierarchical structural analysis and economic evaluations for utilisation in the industry. The report proposes a complete methodology to accurately select new technologies. A computer based prototype model has been developed to handle the more complex fuzzy calculations. Decision-makers are only required to express their opinions on comparative importance of various factors in linguistic terms rather than exact numerical values. These linguistic variable scales, such as ‘very high’, ‘high’, ‘medium’, ‘low’ and ‘very low’, are then converted into fuzzy numbers, since it becomes more meaningful to quantify a subjective measurement into a range rather than in an exact value. By aggregating the hierarchy, the preferential weight of each alternative technology is found, which is called fuzzy appropriate index. The fuzzy appropriate indices of different technologies are then ranked and preferential ranking orders of technologies are found. From the economic evaluation perspective, a fuzzy cash flow analysis is employed. This deals quantitatively with imprecision or uncertainties, as the cash flows are modelled as triangular fuzzy numbers which represent ‘the most likely possible value’, ‘the most pessimistic value’ and ‘the most optimistic value’. By using this methodology, the ambiguities involved in the assessment data can be effectively represented and processed to assure a more convincing and effective decision- making process when selecting new technologies in which to invest. The prototype model was validated with a case study within the aviation industry that ensured it was properly configured to meet the

Uncertainty Assessment in High-Risk Environments Using Probability, Evidence Theory and Expert Judgment Elicitation

The level of uncertainty in advanced system design is assessed by comparing the results of expert judgment elicitation to probability and evidence theory. This research shows how one type of monotone measure, namely Dempster-Shafer Theory of Evidence can expand the framework of uncertainty to provide decision makers a more robust solution space. The issues imbedded in this research are focused on how the relevant predictive uncertainty produced by similar action is measured. This methodology uses the established approach from traditional probability theory and Dempster-Shafer evidence theory to combine two classes of uncertainty, aleatory and epistemic. Probability theory provides the mathematical structure traditionally used in the representation of aleatory uncertainty. The uncertainty in analysis outcomes is represented by probability distributions and typically summarized as Complimentary Cumulative Distribution Functions (CCDFs). The main components of this research are probability of X in the probability theory compared to mx in evidence theory. Using this comparison, an epistemic model is developed to obtain the upper “CCPF - Complimentary Cumulative Plausibility Function” limits and the lower “CCBF - Complimentary Cumulative Belief Function” limits compared to the traditional probability function. A conceptual design for the Thermal Protection System (TPS) of future Crew Exploration Vehicles (CEV) is used as an initial test case. A questionnaire is tailored to elicit judgment from experts in high-risk environments. Based on description and characteristics, the answers of the questionnaire produces information, that serves as qualitative semantics used for the evidence theory functions. The computational mechanism provides a heuristic approach for the compilation and presentation of the results. A follow-up evaluation serves as validation of the findings and provides useful information in terms of consistency and adoptability to other domains. The results of this methodology provide a useful and practical approach in conceptual design to aid the decision maker in assessing the level of uncertainty of the experts. The methodology presented is well-suited for decision makers that encompass similar conceptual design instruments

Anticipation and Risk – From the inverse problem to reverse computation

Abstract. Risk assessment is relevant only if it has predictive relevance. In this sense, the anticipatory perspective has yet to contribute to more adequate predictions. For purely physics-based phenomena, predictions are as good as the science describing such phenomena. For the dynamics of the living, the physics of the matter making up the living is only a partial description of their change over time. The space of possibilities is the missing component, complementary to physics and its associated predictions based on probabilistic methods. The inverse modeling problem, and moreover the reverse computation model guide anticipatory-based predictive methodologies. An experimental setting for the quantification of anticipation is advanced and structural measurement is suggested as a possible mathematics for anticipation-based risk assessment

Fuzzy Systems in Business Valuation

This research aims to develop a model that is able to integrate and objectify information provided by the different business valuation methods, incorporating quality management in its formal approach, which to date has not been considered in the literature about business valuation or quality management. Firstly, the company is valued using the methods which best adapt to its specific characteristics. Because of the subjectivity inherent in any valuation process, the results will be expressed through Triangular Fuzzy Numbers (TFN). These Fuzzy Numbers will be aggregated and summarized by applying Basic Defuzzification Distribution Uncertain Probabilistic Ordered Weighted Averaging operator (BADD-UPOWA). The weighting factors will be: the degree of confidence in each of the business valuation methods applied, and the innovative use of the company’s position on Crosby’s Quality Administration Grid. The results from application of the model in a case study show a significant reduction in uncertainty in contrast to the initial valuations. Moreover, the proposed methodology is seen to increase the final value of the company as its advances in quality management

Uncertainty-based decision-making in fire safety: Analyzing the alternatives

Large accidents throughout the 20th century marked the development of safety fields in engineering, devoted to better identify hazards, understand risks and properly manage them. As these fields evolved rather quickly and moved from a compliance to a risk-based approach, a significant delay in this transition was experienced in fire safety engineering (FSE). Devastating fires well into the 21st century and the restrictive nature of prescriptive codes signaled the need to transition towards a performance-based one. A performance-based approach provides flexibility and capitalizes on learning from accidental events and engineering disciplines such as process safety and FSE. This work provides an overview of the main alternatives to account for uncertainty in safety studies within the context of FSE, including traditional probabilistic analyses and emerging approaches such as strength of knowledge. A simple example is used to illustrate the impact of the uncertainty analysis on the results of a simple fire safety assessment. A structured evaluation is performed on each alternative to assess its ease of implementation and communication. The outcome is a compendium of advantages and disadvantages of the alternatives that constitute a toolbox for fire safety engineers to configure and use within their fire risk assessments. Process safety engineers are expected to gain an understanding of the similar and important challenges of FSE, being it directly relevant for process risk management and fire risk management in administrative buildings

Landscapes of Danger: A Geospatial Analysis of Perceived and Realistic Risk in Bryce Canyon National Park

The quantification of risk has inspired a wide breath of literature from the physical sciences, social sciences, and interdisciplinary disciplines like geography. Many attempts to estimate risk via natural hazards either focus on quantifying realistic risk or perceived risk of lay persons, with very little overlap between these paradigms. Due to this, a considerable knowledge gap exists within perceived risk and natural hazards research. This study aims to provide a comprehensive, risk estimation and assessment strategy through a multi-hazard risk assessment of Bryce Canyon National Park (BRCA). This case study analyzed knowledge of risk among visitors with perception surveys and Likert-based scales, in addition to identifying high risk areas of the park through Geographic Information Systems (gis). With a sample size of 254, a systematic stratified sampling method was implemented at specific sites in the park chosen for their distinctive viewsheds, accessibility, and popularity. To identify risky areas, two fuzzy logic models were built: one to identify areas susceptible to rockfall and another to identify areas susceptible to landslides/slumps. Overall, respondents reported feeling largely unconcerned when ranking their perception of various risks within the park (µ = 2.1, σ = .78), however, perception gaps and demographic influences were revealed on individual event types. When asked to identify dangerous areas of the park, participants tended to select locations in the main amphitheater – the most highly trafficked area of the park – even though the fuzzy logic models showed a wider range of locations were susceptible to mass wasting events