Evaluation of supply chain default risk based on fuzzy influence diagram

After introducing the supply chain default risk and its causes,based on the literature review of the evaluation methods of supply chain risks,a new evaluation method called the fuzzy influence diagram which combines fuzzy sets with influence diagram theory and considers the interaction among risk factors is proposed.Furthermore,an evaluation model of the supply chain default risk is established based on the research of default risk evaluation and the fuzzy influence diagram.First,the model takes the loss of risk as a valuable node,risk factors as random nodes,drawing a risk analysis influence diagram.Then,three kinds of fuzzy sets are defined,including state fuzzy sets,probabilistic fuzzy sets and a relation fuzzy matrix.Finally,by using the fuzzy algorithm to evaluate nodes,the probability of risk occurrence and the degrees of risk loss are obtained.On the basis of the model,an instance application is used to prove its utility and effectiveness.

A Fuzzy Probabilistic Influence Diagram Method to Assess Marine Configuration Risk

A risk assessment method for marine configuration based on Fuzzy Probability Influence Diagram （FPID） and Failure Mode and Effect Analysis （FMEA） is established in this paper. Considering the fuzzy characteristic of the two key inputs such as event happening probability and relation probability, the method induces fuzzy probability into the PID risk assessment for marine configuration, where defuzzification is performed using the centroid method to determine the risk at a given grade of a probabilistic item. FMEA as a traditional qualitative analysis method is used to determine the effect factor structure. An application of the presented method for the offshore jacket platform is implemented. The method can be widely applicable although only offshore platform is analyzed here.

Sequential maneuvering decisions based on multi-stage influence diagram in air combat

A multi-stage influence diagram is used to model the pilot＇s sequential decision making in one on one air combat. The model based on the multi-stage influence diagram graphically describes the elements of decision process, and contains a point-mass model for the dynamics of an aircraft and takes into account the decision maker＇s preferences under uncertain conditions. Considering an active opponent, the opponent＇s maneuvers can be modeled stochastically. The solution of multistage influence diagram can be obtained by converting the multistage influence diagram into a two-level optimization problem. The simulation results show the model is effective.

Functionality evaluation of system of systems architecture based on extended influence diagrams

System of systems architecture（SoSA） has received increasing emphasis by scholars since Zachman ignited its flame in 1987. Given its complexity and abstractness, it is critical to validate and evaluate SoSA to ensure requirements have been met.Multiple qualities are discussed in the literature of SoSA evaluation, while research on functionality is scarce. In order to assess SoSA functionality, an extended influence diagram（EID） is developed in this paper. Meanwhile, a simulation method is proposed to elicit the conditional probabilities in EID through designing and executing SoSA. An illustrative anti-missile architecture case is introduced for EID development, architecture design, and simulation.

Intelligent reasoning and management decision making with grey rough influence diagrams

Purpose-Influence diagrams(IDs)have been widely applied as a form of knowledge expression and a decision analysis tool in the management and engineering fields.Relationship measurements and expectation values are computed depending on probability distributions in traditional IDs,however,most information systems in the real world are nondeterministic,and data in information tables can be interval valued,multiple valued and even incomplete.Consequently,conventional numeric models of IDs are not suitable for information processing with respect to imprecise data whose boundaries are uncertain.The paper aims to discuss these issues.Design/methodology/approach-The grey system theory and rough sets have proved to be effective tools in the data processing of uncertain information systems,approximate knowledge acquisition and representation are also the objectives in intelligent reasoning and decision analysis.Hence,this study proposes a new mathematical model by combining grey rough sets with IDs,and approximate measurements are used instead of probability distribution,an implicational relationship is utilized instead of an indiscernible relationship,and all of the features of the proposed approach contribute to deal with uncertain problems.Findings-The focus of this paper is to provide a more comprehensive framework for approximate knowledge representation and intelligent decision analysis in uncertain information systems and an example of decision support in product management systems with the new approach is illustrated.Originality/value-Collaboration of IDs and grey rough sets is first proposed,which provides a new mathematical and graphical tool for approximate reasoning and intelligent decision analysis within interval-valued information systems.

A Capital Investment Appraisal Model Based Cognitive Influence Diagrams

In this paper, we first analyse the shortcomings of existing investment appraisal methodologs, and present the theory of cognitive influence diagrams, which is constructed by integrating cognitive maps and influence diagrams. Simulation is used to solve the probabilistic inference by using the sample spare of each random variable involved in the analysis. A dynamic criterion is supposed based on the separable nature of value function. With this criterion. We combine influence diagrams with dynamic economic assessment. Thus the dynamic characteristics of investment proposal can be evaluated.

Reducing Threats by Using Bayesian Networks to Prioritize and Combine Defense in Depth Security Measures

Studied in this article is whether the Bayesian Network Model (BNM) can be effectively applied to the prioritization of defense in-depth security tools and procedures and to the combining of those measures to reduce cyber threats. The methods used in this study consisted of scanning 24 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals using the Likert Scale Model for the article’s list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce. The defense in depth tools and procedures are then compared to see whether the Likert scale and the Bayesian Network Model could be effectively applied to prioritize and combine the measures to reduce cyber threats attacks against organizational and private computing systems. The findings of the research reject the H0 null hypothesis that BNM does not affect the relationship between the prioritization and combining of 24 Cybersecurity Article’s defense in depth tools and procedures (independent variables) and cyber threats (dependent variables).