117,465 research outputs found
A fuzzy approach to risk based decision making
Decision making is a tough process. It involves dealing with a lot of uncertainty and projecting what the final outcome might be. Depending on the projection of the uncertain outcome, a decision has to be made. In a peer-to-peer financial interaction the trusting agent, in order to analyse the Risk, has to consider the possible likelihood of failure of the interaction and the possible consequences of failure to its resources involved in the interaction before concluding whether to interact with the probable trusted agent or not. Further, it may also have to choose and decide on an agent to interact with from a set of probable trusted agents. In this paper, we propose a fuzzy risk based decision making system that would assist the trusting agent to ease its decision making process
Interval type–2 fuzzy decision making
This paper concerns itself with decision making under uncertainty and theconsideration of risk. Type-1 fuzzy logic by its (essentially) crisp nature is limited in modelling decision making as there is no uncertainty in the membership function. We are interested in the role that interval type–2 fuzzy sets might play in enhancing decision making. Previous work by Bellman and Zadeh considered decision making to be based on goals and constraint. They deployed type–1 fuzzy sets. This paper extends this notion to interval type–2 fuzzy sets and presents a new approach to using interval type-2 fuzzy sets in a decision making situation taking into account the risk associated with the decision making. The explicit consideration of risk levels increases the
solution space of the decision process and thus enables better decisions. We explain the new approach and provide two examples to show how this new approach works
Risk Identifier of Electronic Procurement Process based on Fuzzy AHP and AHP Method
Risk identifier is a vital function for electronic procurement system. To avoid risk potentialities, a knowledge base is developed which provides risk messages to users to mitigate the risk in the corresponding area of risk attributes defined and an acceptable vulnerability is provided to users through algorithm execution. Multiple Criteria Decision Making (MCDM) and risk mitigation algorithm is the key strength for the newly developed system model of risk removal. Both Fuzzy and AHP based MCDM approach has been executed and the results have been compared. Keywords: Fuzzy Pair wise decision matrix, Risk Identifier, Risk Mitigation Algorithm, Multi Criteria Decision Making (MCDM), AH
A fuzzy dynamic inoperability input-output model for strategic risk management in global production networks
Strategic decision making in Global Production Networks (GPNs) is quite challenging, especially due to the unavailability of precise quantitative knowledge, variety of relevant risk factors that need to be considered and the interdependencies that can exist between multiple partners across the globe. In this paper, a risk evaluation method for GPNs based on a novel Fuzzy Dynamic Inoperability Input Output Model (Fuzzy DIIM) is proposed. A fuzzy multi-criteria approach is developed to determine interdependencies between nodes in a GPN using experts’ knowledge. An efficient and accurate method based on fuzzy interval calculus in the Fuzzy DIIM is proposed. The risk evaluation method takes into account various risk scenarios relevant to the GPN and likelihoods of their occurrences. A case of beverage production from food industry is used to showcase the application of the proposed risk evaluation method. It is demonstrated how it can be used for GPN strategic decision making. The impact of risk on inoperability of alternative GPN configurations considering different risk scenarios is analysed
Evaluating high risks in large-scale projects using an extended VIKOR method under a fuzzy environment
The complexity of large-scale projects has led to numerous risks in their life cycle. This paper presents a new risk evaluation approach in order to rank the high risks in large-scale projects and improve the performance of these projects. It is based on the fuzzy set theory that is an effective tool to handle uncertainty. It is also based on an extended VIKOR method that is one of the well-known multiple criteria decision-making (MCDM) methods. The proposed decision-making approach integrates knowledge and experience acquired from professional experts, since they perform the risk identification and also the subjective judgments of the performance rating for high risks in terms of conflicting criteria, including probability, impact, quickness of reaction toward risk, event measure quantity and event capability criteria. The most notable difference of the proposed VIKOR method with its traditional version is just the use of fuzzy decision-matrix data to calculate the ranking index without the need to ask the experts. Finally, the proposed approach is illustrated with a real-case study in an Iranian power plant project, and the associated results are compared with two well-known decision-making methods under a fuzzy environment
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Knowledge dependencies in fuzzy information systems evaluation
Experience and research within the field of Information Systems Evaluation (ISE), has traditionally centered on providing tools and techniques for investment justification and appraisal, based upon explicit knowledge which encodes financial and other direct situational factors (such as accounting, costing and risk metrics). However, such approaches tend not to include additional causal interdependencies that are based upon tacit knowledge and are inherent within such a decision-making task. The authors show the results of applying a cognitive mapping approach, in the guise of a Fuzzy Cognitive Mapping (FCM) simulation, i.e. Fuzzy Information Systems Evaluation (F-ISE), in order to highlight the usefulness of applying such a technique. The authors highlight those contingent and necessary knowledge dependencies, in an exploratory sense, which relate to the investment appraisal decision-making task, in terms of the interplay between tacit and explicit knowledge, in this regard
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A decision support model for management of fuzziness in global risk assessment
Solving decision-making problems requires efficient handling of uncertainties. This task has been usually performed by means of expert systems which are based on classical logic and, therefore, need special methods such as heuristic approaches, probability theory, possibility theory, and fuzzy theory. The later approach, fuzzy reasoning and logic, offers a more natural way of handling uncertainty since it is similar to human logical reasoning. In this paper, we develop a fuzzy logic model for assessment and prediction of country risk. This fuzzy method provides a systematic approach to analyzing a target country. By its nature, the decision making for global market involves various uncertain criteria; therefore, the fuzzy approach is suitable for this kind of analysis. The advantages of the approach are inclusion of economic data, consideration of political/social factors, and the ability to handle exact and fuzzy data
Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories
The present work articulates few case empirical studies on decision making in industrial
context. Development of variety of Decision Support System (DSS) under uncertainty and
vague information is attempted herein. The study emphases on five important decision making
domains where effective decision making may surely enhance overall performance of the
organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier
selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply
chain’s g-resilient index and v) risk assessment in e-commerce exercises.
Firstly, decision support systems in relation to robot selection are conceptualized through
adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey
set theory is also found useful in this regard; and is combined with TODIM approach to
identify the best robot alternative. In this work, an attempt is also made to tackle subjective
(qualitative) and objective (quantitative) evaluation information simultaneously, towards
effective decision making.
Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a
novel decision support framework is proposed to address g-resilient (green and resilient)
supplier selection issues. Green capability of suppliers’ ensures the pollution free operation;
while, resiliency deals with unexpected system disruptions. A comparative analysis of the
results is also carried out by applying well-known decision making approaches like Fuzzy-
TOPSIS and Fuzzy-VIKOR.
In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance-
Based’ model in combination with grey set theory to deal with 3PL provider selection,
considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is
articulated to demonstrate application potential of the proposed model. The results, obtained
thereof, have been compared to that of grey-TOPSIS approach.
Another part of this dissertation is to provide an integrated framework in order to assess gresilient
(ecosilient) performance of the supply chain of a case automotive company. The
overall g-resilient supply chain performance is determined by computing a unique ecosilient
(g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with
Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient
criteria in accordance to their current status of performance.
The study is further extended to analyze, and thereby, to mitigate various risk factors (risk
sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are
recognized and evaluated in a decision making perspective by utilizing the knowledge
acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying
parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying
parameters are assessed in a subjective manner (linguistic human judgment), rather than
exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to
various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk
factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem
context (toward e-commerce success). Risks are now categorized into different levels of
severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic).
Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect
the company’s e-commerce performance are recognized through such categorization. The
overall risk extent is determined by aggregating individual risks (under ‘critical’ level of
severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then
used to obtain structural relationship amongst aforementioned five risk sources. An
appropriate action requirement plan is also suggested, to control and minimize risks associated
with e-commerce exercises
A fuzzy dynamic bayesian network-based situation assessment approach
Situation awareness (SA), a state in the mind of a human, is essential to conduct decision-making activities. It is about the perception of the elements in the environment, the comprehension of their meaning, and the projection of their status in the near future. Two decades of investigation and analysis of accidents have showed that SA was behind of many serious large-scale technological systems' accidents. This emphasizes the importance of SA support systems development for complex and dynamic environments. This paper presents a fuzzy dynamic Bayesian network-based situation assessment approach to support the operators in decision making process in hazardous situations. The approach includes a dynamic Bayesian network-based situational network to model the hazardous situations where the existence of the situations can be inferred by sensor observations through the SCADA monitoring system using a fuzzy quantizer method. In addition to generate the assessment result, a fuzzy risk estimation method is proposed to show the risk level of situations. Ultimately a hazardous environment from U.S. Chemical Safety Board investigation reports has been used to illustrate the application of proposed approach. © 2013 IEEE
Use of fuzzy risk assessment in FMEA of offshore engineering systems
This paper proposes a novel framework for analysing and synthesising engineering system risks on the basis of a generic Fuzzy Evidential Reasoning (FER) approach. The approach is developed to simplify the inference process and overcome the problems of traditional fuzzy rule based methods in risk analysis and decision making. The framework, together with the FER approach has been applied to model the safety of an offshore engineering system. The benchmarking between the new model and a well-established Rule based Inference Methodology using the Evidential Reasoning (RIMER) is conducted to demonstrate its reliability and unique characteristics. It will facilitate subjective risk assessment in different engineering systems where historical failure data is not available in their safety practice
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