Study on Nuclear Accident Precursors Using AHP and BBN

Most of the nuclear accident reports used to indicate the implicit precursors which are not easily quantified as underlying factors. The current Probabilistic Safety Assessment (PSA) is capable of quantifying the importance of accident causes in limited scope. It was, therefore, difficult to achieve quantifiable decision-making for resource allocation. In this study, the methodology which facilitates quantifying these precursors and a case study were presented. First, four implicit precursors have been obtained by evaluating the causality and hierarchy structure of various accident factors. Eventually, it turned out that they represent the lack of knowledge. After four precursors are selected, subprecursors were investigated and their cause-consequence relationship was implemented by Bayesian Belief Network (BBN). To prioritize the precursors, the prior probability is initially estimated by expert judgment and updated upon observations. The pair-wise importance between precursors is calculated by Analytic Hierarchy Process (AHP) and the results are converted into node probability tables of the BBN model. Using this method, the sensitivity and the posterior probability of each precursor can be analyzed so that it enables making prioritization for the factors. We tried to prioritize the lessons learned from Fukushima accident to demonstrate the feasibility of the proposed methodology

System Safety Modeling of Alternative Geofencing Configurations for small UAS

As is well known, the integration of small Unmanned Aircraft Systems (sUAS) or “drones” into the National Airspace System (NAS) has captured significant industry, academic, regulatory and media attention. For sUAS that typically fly low and slow, the possibility of a mid-air collision with a nearby general aviation aircraft needs to be studied from a system safety perspective to identify possible hazards and to assess mitigations. The Aviation System Risk Model (ASRM) is a first-generation socio-technical model that uses a Bayesian Belief Network (BBN) methodology to integrate possible hazards to assess a non-linear safety risk metric. Using inductive logic, the ASRM may be used to evaluate underlying causal factors linked to the air vehicle and/or to the systems and procedures that lead to the unsafe state and the probabilistic interactions among these factors that contribute to the safety risk. The ASRM can also assess the projected impact of mitigations. Recently, the ASRM has been updated with the use of the Hazard Classification and Analysis System (HCAS) that provides an analytic structure for categorizing hazards related to the UAS, Airmen, Operations and the Environment. In this paper, the ASRM, together with the HCAS, is demonstrated with a notional scenario that involves a sUAS being used for aerial surveillance in the siting of a wind turbine farm near the Yukon River in Alaska. It is conjectured that the sUAS interacts with a general aviation aircraft flying in the nearby vicinity from a local airport. The sUAS being used is a fixed wing-type where there is a failure of the separation assurance function since the sUAS leaves its Area of Operation (AO) due to a Ground Control Station (GCS) transmission disruption (from faulty maintenance) and by the waypoints being incorrectly programmed. In the modeling approach, the time-dependent effects of wind velocity, wind sensor faults, and wind sensor accuracy are also included. In particular, the system safety study focuses on investigating the mitigating efficacy of alternative geofencing configurations

Human reliability analysis: exploring the intellectual structure of a research field

Humans play a crucial role in modern socio-technical systems. Rooted in reliability engineering, the discipline of Human Reliability Analysis (HRA) has been broadly applied in a variety of domains in order to understand, manage and prevent the potential for human errors. This paper investigates the existing literature pertaining to HRA and aims to provide clarity in the research field by synthesizing the literature in a systematic way through systematic bibliometric analyses. The multi-method approach followed in this research combines factor analysis, multi-dimensional scaling, and bibliometric mapping to identify main HRA research areas. This document reviews over 1200 contributions, with the ultimate goal of identifying current research streams and outlining the potential for future research via a large-scale analysis of contributions indexed in Scopus database

Supply chain risk management : systematic literature review and a conceptual framework for capturing interdependencies between risks

The purpose of this research is to conduct a comprehensive and systematic review of the literature in the field of 'Supply Chain Risk Management' and identify important research gaps for potential research. Furthermore, a conceptual risk management framework is also proposed that encompasses holistic view of the field. 'Systematic Literature Review' method is used to examine quality articles published over a time period of almost 15 years (2000 - June, 2014). The findings of the study are validated through text mining software. Systematic literature review has identified the progress of research based on various descriptive and thematic typologies. The review and text mining analysis have also provided an insight into major research gaps. Based on the identified gaps, a framework is developed that can help researchers model interdependencies between risk factors

Development and Application of Spaceflight Performance Shaping Factors for Human Reliability Analysis

The ability of crewmembers to perform various critical functions during spacecraft operations is widely recognized as being essential to mission success. This necessity motivates the desire to better characterize factors that can influence crewmember performance so that those with positive effects can be enhanced, while those with negative impacts can be minimized. Established Human Reliability Analysis methods exist for analyzing performance within the context of myriad terrestrial scenarios. Many of the existing methods have their roots in nuclear power plant operations. While perhaps similar, the factors influencing performance traditionally used in these methods do not take into account the unique conditions encountered during spaceflight. Therefore, this research has identified a tailored set of factors that influence human task performance during space missions. This thesis describes an organizational scheme developed to aid in classifying and communicating the factors across disciplines and organizations. Definitions of identified factors are given for the spaceflight-specific context. A visual display of the factors, called the Contributing Factor Map, is presented and its use as a risk communication tool is discussed. The Bayesian Network is discussed as a quantification approach allowing relationships between factors, in addition to the factor relationships to performance outcomes, to be modeled. A method for determining a network structure was developed for domains such as human spaceflight, in which a global set of data for analysis is not available. This method applied the Analytic Hierarchy Process, and causal latency concepts from the Human Factors Analysis and Classification System in a novel way to guide choices for modeling the dominant set of factors and relationships in a simplified Bayesian Network structure. In addition, an approach for modeling the factors as statistical variables in a Bayesian Network making use of existing design requirements and human performance data is discussed. Applications of this modeling approach in terms of requirement completeness assessment and identification of future research needs are also described. Finally, an illustrative quantified Bayesian Network for the spaceflight domain is given, built on the factor identification and structure development work throughout the thesis. Its use in a Human Reliability Analysis is demonstrated

A Comprehensive Decision Support Framework in the Front-End Phase of Major Transportation Projects

Identifying the best project alternative is a critical challenge facing major transportation projects (MTPs) at the front-end phase. The increasing complexity and dynamism of MTPs have imposed substantial uncertainties and subjectivities in the decision-making process. Despite the efforts made in previous studies, a stochastic framework to facilitate the comprehensive assessment is still missing. In this research, a stochastic decision support framework has been developed to cope with the considerable uncertainties in MTPs. The features of the proposed decision support framework are achieved by using the Bayesian belief network modeling technique to provide a comprehensive registry of the relevant decision factors, establish the interrelationships between these decision factors, and consequently quantify uncertainties of decision indicators. The calculated probabilities for decision indicators have been interpreted to a satisfaction level of stakeholders based on their constraints as a multi-criteria decision model. A Monte Carlo simulation has been conducted to simulate a real condition using the decision indicators probability as input. Finally, MTP alternatives prioritized according to the anticipated satisfactory gained among various stakeholders. The created framework is used in a preliminary alternative assessment for case study related to Detroit River International Crossing project. The case study investigates the decision-making of key stakeholders related to prioritization of alternative projects for a new access between Detroit, US and Winsdor, Canada. The project team verified applicability of the model. The developed framework and the case study highlight the significance of identification of a stochastic project alternative assessment method. The proposed framework provides decision-makers with a decision support tool to facilitate front-end phase of MTPs

The Creation Of Tools And Models To Characterize And Quantify User-centered Design Considerations In Product And System Developm

Ease of use differentiates products in a highly competitive market place. It also brings an added value that culminates in a higher degree of customer satisfaction, repeated business, increased sales, and higher revenue. User-centered design is a strategic asset that companies can use to improve their customer relationships by learning more about their customers, and increase their sales. In today\u27s economy, the measurement of intangible assets such as user experience has become a major need for industries because of the relationship between user-centered design and organizational benefits such as customer loyalty. As companies realize that the inclusion of user-centered design concepts in product or system design are a key component of attracting and maintaining customers, as well as increasing revenue, the need for quantitative methods to describe these benefits has become more urgent. The goal of this research is to develop a methodology to characterize user-centered design features, customer benefits and organizational benefits resulting from developing products using user-centered design principles through the use of an integrated framework of critical factors. Therefore, this research focuses on the identification of the most significant variables required to assess and measure the degree of user-centered design (UCD) characteristics included in the various aspects of product development such as physical design features, cognitive design attributes, industrial design aspects and user experience design considerations. Also this research focuses on the development of assessment tools for developers to use when evaluating the incorporation of user-centered design features in the creation of products and systems. In addition, a mathematical model to quantify the inclusion of UCD factors considered in the design of a product and systems is presented in this research. The results obtained using the assessment tools and the mathematical model can be employed to assess the customer benefits and organizational benefits resulting from including user-centered design features in the creation of products and systems. Overall, organizational benefits such as customer loyalty, company image, and profitability are expected to be impacted by the company\u27s capability to meet or exceed stated design claims and performance consistency while maintaining aesthetic appeal, long product life, and product usefulness. The successful completion of this research has produced many beneficial research findings. For example, it has helped characterize and develop descriptors for estimating critical quantitative and qualitative components, sub-components, and factors influencing user-centered design that are related to customer and organizational benefits through the use of fuzzy set modeling. In addition, the development of specific tools, methods, and techniques for evaluating and quantifying UCD components resulted from this study

A FRAMEWORK FOR STRATEGIC PROJECT ANALYSIS AND PRIORITIZATION

Projects that support the long-term strategic intent and alignment are considered strategic projects. Therefore, these projects must consider their alignment with the organization’s current strategy and focus on the risk, organizational capability, resources availability, political influence, and socio-cultural factors. Quantitative and qualitative methods prioritize the projects; however, they are usually suitable for specific industries. Although prioritization models are used in the private sector, the same in the public sector is not widely seen in the literature. The lack of models in the public sector has happened because of the projects’ social implications, the value perception of different projects in the public sector, and potentially differing value perceptions attached to the types of projects in different decision-making environments in the public sector. The thesis proposes a generic framework to develop a priority list of the available basket of projects and decide on projects for the next undertaking. The focus of the thesis is on public projects. The analysis in the framework considers the critical factors for prioritization obtained from the literature clustered through the agglomerative text clustering technique. In the proposed framework, 13 critical clusters are identified and weighted using the Criteria Importance Through Intercriteria Correlation (CRITIC) method to develop their ranking using the Technique for Order of Preference Similarity Ideal Solution (TOPSIS) method. In addition, the proposed framework uses vector weighting to prioritize projects across industries. The applicability of the framework is demonstrated through Qatar’s real estate and transportation projects. The outcome obtained from the framework is compared with those obtained through the experts using the System Usability Scale (SUS). The comparison shows that the framework provides good predictability of the projects for implementation