Group Decision Making for a Fuzzy Software Quality Assessment Model to Evaluate User Satisfaction

Information techniques have brought us tremendous benefit, whereas people are increasingly depended on lots of information systems. Therefore, how to establish an assessment model to choose a better software quality suitable for end-users is an important issue. This study is to present an algorithm of the group decision makers with crisp or fuzzy weights to tackle the integrated software quality for evaluating user satisfaction using fuzzy set theory, where the grades of quality and the grade of importance of quality items are assessed by linguistic values represented by triangular fuzzy numbers. The proposed algorithm is more flexible and useful than the ones that have presented before, since the weights against decision makers are considered

A fuzzy outranking approach in risk analysis of web service security

Risk analysis is considered as an important process to identify the known and potential vulnerabilities and threats in the web services security. It is quite difficult for users to collect adequate events to estimate the full vulnerabilities and probability of threats in the Web, due to the rapid change of the malicious attacks and the new computer’s vulnerabilities. In this paper, a fuzzy risk assessment model is developed in order to evaluate the risk of web services in a situation where complete information is not available. The proposed model extends Pseudo-Order Preference Model (POPM) to estimate the imprecise risk based on richness of information and to determine their ranking using a weighted additive rule. A case study of a number of web services is presented in order to test the proposed approach

Advanced Quantitative Risk Assessment of Offshore Gas Pipeline Systems

This research has reviewed the current status of offshore and marine safety. The major problems identified in the research are associated with risk modelling under circumstances where the lack of data or high level of uncertainty exists. This PhD research adopts an object-oriented approach, a natural and straightforward mechanism of organising information of the real world systems, to represent the Offshore Gas Supply Systems (OGSSs) at both the component and system levels. Then based on the object-oriented approach, frameworks of aggregative risk assessment and fault tree analysis are developed. Aggregative risk assessment is to evaluate the risk levels of components, subsystems, and the overall OGSS. Fault trees are then used to represent the cause-effect relationships for a specific risk in the system. Use of these two assessment frameworks can help decision makers to obtain comprehensive view of risks in the OGSS. In order to quantitatively evaluate the framework of aggregative risk, this thesis uses a fuzzy aggregative risk assessment method to determine the risk levels associated with components, subsystems, and the overall OGSS. The fuzzy aggregative risk assessment method is tailored to quantify the risk levels of components, subsystems, and the OGSS. The proposed method is able to identify the most critical subsystem in the OGSS. As soon as, the most critical subsystem is identified, Fuzzy Fault Tree Analysis (FFTA) is employed to quantitatively evaluate the cause-effect relationships for specific undesired event. These results can help risk analysts to select Risk Control Options (RCOs) for mitigating risks in an OGSS. It is not financially possible to employ all the selected RCOs. Therefore, it is necessary to rank and select the best RCO. A decision making method using the Fuzzy TOPSIS (FTOPSIS) is proposed to demonstrate the selection of the best RCOs to control the existing risks in the system. The developed models and frameworks can be integrated to formulate a platform which enables to facilitate risk assessment and safety management of OGSSs without jeopardising the efficiency of OGSSs operations in various situations where traditional risk assessment and safety management techniques cannot be effectively applied

Food security risk level assessment : a fuzzy logic-based approach

A fuzzy logic (FL)-based food security risk level assessment system is designed and is presented in this article. Three inputs—yield, production, and economic growth—are used to predict the level of risk associated with food supply. A number of previous studies have related food supply with risk assessment for particular types of food, but none of the work was specifically concerned with how the wider food chain might be affected. The system we describe here uses the Mamdani method. The resulting system can assess risk level against three grades: severe, acceptable, and good. The method is tested with UK (United Kingdom) cereal data for the period from 1988 to 2008. The approach is discussed on the basis that it could be used as a starting point in developing tools that may either assess current food security risk or predict periods or regions of impending pressure on food supply

Hierarchical risk assessment of water supply systems

Water supply systems are usually designed, constructed, operated, and managed in an open environment, thus they are inevitably exposed to varied uncertain threats and conditions. In order to evaluate the reliability of water supply systems under threatened conditions, risk assessment has been recognised as a useful tool to identify threats, analyse vulnerabilities and risks, and select proper mitigation measures. However, due to the complexity and uncertainty of water supply systems and risks, consistent and effective assessments are hard to accomplish by using available risk techniques. With respect to this, the current study develops a new method to assess the risks in complex water supply systems by reconsidering the organisation of risk information and risk mechanism based on the concepts of object-oriented approach. Then hierarchical assessments are conducted to evaluate the risks of components and the water supply system. The current study firstly adopts object-oriented approach, a natural and straightforward mechanism of organising information of the real world systems, to represent the water supply system at both component and system levels. At the component level, components of a water supply system are viewed as different and functional objects. Associated with each object, there are states transition diagrams that explicitly describe the risk relationships between hazards/threats, possible failure states, and negative consequences. At the system level, the water supply system is viewed as a network composed of interconnected objects. Objectoriented structures of the system represent the whole/part relationships and interconnections between components. Then based on the object states transition diagrams and object-oriented structures, this study develops two types of frameworks for risk assessment, i.e., framework of aggregative risk assessment and framework of fault tree analysis. Aggregative risk assessment is to evaluate the risk levels of components, subsystems, and the overall water supply system. While fault trees are to represent the cause-effect relationships for a specific risk in the system. Assessments of these two frameworks can help decision makers to prioritise their maintenance and management strategies in water supply systems. In order to quantitatively evaluate the framework of aggregative risk, this thesis uses a fuzzy evidential reasoning method to determine the risk levels associated with components, subsystems, and the overall water supply system. Fuzzy sets theory is used to evaluate the likelihood, severity, and risk levels associated with each hazard. Dempster-Shafer theory, a typical evidential reasoning method, is adopted to aggregate the risk levels of multiple hazards along the hierarchy of aggregative risk assessment to generate risk levels of components, subsystems, and the overall water supply system. Although fuzzy sets theory and Dempster-Shafer theory have been extensively applied to various problems, their potential of conducting aggregative risk assessments is originally explored in this thesis. Finally, in order to quantitatively evaluate the cause-effect relationships in a water supply system, fuzzy fault tree analysis is adopted in this study. Results of this analysis are likelihood of the occurrence for a specific event and importance measures of the possible contributing events. These results can help risk analysts to plan their mitigation measures to effectively control risks in the water supply system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Method and approach Mapping for Agri-food Supply Chain Risk Management: A literature review

One of the agri-food characteristics is perishable product which made it has a higher chance damage risk from the farmer to the consumer. While issues around food security and associated risks are extremely important. Some methods or approaches have been used to identify and assess risks that occur in agri-food supply chain. The purpose of this paper was to identify the development of methods or approaches used to identify and assess the risks that occured in the agri-food supply chain. The articles search was undertaken through articles search on selected relevant journals of supply chain risk management for agri-food published from 2004 until 2014. A total of 77 randomly selected journal articles had been analyzed. These mapping were arranged in systematic stages, started from searches related supply chain risk management for agrifood. Furthermore, the articles identified and classified the methods or approaches for each stage of supply chain risk management, and were divided into three main stages: risk identification, risk assessment and risk mitigation. The last, the articles of risk identification are categorized into three groups : qualitative, semi-quantitative and qualitative.The mapping results showed that risk assessment supply chain for agri-food was much related to microbiology risk assessment. It related to the characteristics of agri-food products. Standard models used for risk assessment in supply chain for agri-food were based on integration of statistical analysis and simulation. The other techniques used included intelligent technique, optimization models and multi-criteria decision analysis. The literature on supply chain risk management for agri-food is so vast, complex and difficult to understand that a mapping of method and approach is needed and much value for the research community. Keywords :supply chain risk, risk identification, risk assessment, risk mitigation, agri-foo

An integrated fuzzy risk assessment for seaport operations

Seaport operations are characterised by high levels of uncertainty, as a result their risk evaluation is a very challenging task. Much of the available data associated with the system’s operations is uncertain and ambiguous, requiring a flexible yet robust approach of handling both quantitative and qualitative data as well as a means of updating existing information as new data becomes available. Conventional risk modelling approaches are considered to be inadequate due to the lack of flexibility and an inappropriate structure for addressing the system’s risks. This paper proposes a novel fuzzy risk assessment approach to facilitating the treatment of uncertainties in seaport operations and to optimise its performance effectiveness in a systematic manner. The methodology consists of a fuzzy analytical hierarchy process, an evidential reasoning (ER) approach, fuzzy set theory and expected utility. The fuzzy analytical hierarchy process is used to analyse the complex structure of seaport operations and determine the weights of risk factors while ER is used to synthesise them. The methodology provides a robust mathematical framework for collaborative modelling of the system and allows for a step by step analysis of the system in a systematic manner. It is envisaged that the proposed approach could provide managers and infrastructure analysts with a flexible tool to enhance the resilience of the system in a systematic manner

A multi-scale method to assess pesticide contamination risks in agricultural watersheds

The protection of water is now a major priority for environmental managers, especially around drinkingpumping stations. In view of the new challenges facing water agencies, we developed a method designedto support their public policy decision-making, at a variety of different spatial scales. In this paper, wepresent this new spatial method, using remote sensing and a GIS, designed to determine the contami-nation risk due to agricultural inputs, such as pesticides. The originality of this method lies in the useof a very detailed spatial object, the RSO (Reference Spatial Object), which can be aggregated to manyworking and managing scales. This has been achieved thanks to the pixel size of the remote sensing, witha grid resolution of 30 m × 30 m in our application.The method – called PHYTOPIXAL – is based on a combination of indicators relating to the environmen-tal vulnerability of the surface water environment (slope, soil type and distance to the stream) and theagricultural pressure (land use and practices of the farmers). The combination of these indicators for eachpixel provides the contamination risk. The scoring of variables was implemented according knowledgein literature and of experts.This method is used to target specific agricultural input transfer risks. The risk values are first calculatedfor each pixel. After this initial calculation, the data are then aggregated for decision makers, accordingto the most suitable levels of organisation. These data are based on an average value for the watershedareas.In this paper we detail an application of the method to an area in the hills of Southwest France. Weshow the pesticide contamination risk by in areas with different sized watersheds, ranging from 2 km2to 7000 km2, in which stream water is collected for consumption by humans and animals. The resultswere recently used by the regional water agency to determine the protection zoning for a large pumpingstation. Measures were then proposed to farmers with a view to improving their practices.The method can be extrapolated to different other areas to preserve or restore the surface water

Decision Support Systems

Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference