Uncertain multiattribute decision making method based on entropy and evidential reasoning approach

This paper illustrates a subjective safety-cost based design-decision support framework using fuzzy logic and evidential reasoning approaches. The framework is divided into three parts. The first one is for safety estimate and synthesis including fuzzy rule-based safety estimate using fuzzy rule-based evidential reasoning (FRB-ER) approach, as well as the safety synthesis using evidential reasoning approach. Considering the cost incurred for the safety improvement in a design option, the second part focuses on synthesising the safety and cost estimates using evidential reasoning approach to obtain the overall evaluation of the whole system for each design option. The third part is to apply the overall evaluation for the design selection. An illustrative example is used to demonstrate the application of the proposed framework.

The Evidential Reasoning (ER) approach is a general approach for analyzing multiple criteria decision problems under various types of uncertainty using a unified framework—belief structure. In this paper, the ER approach is surveyed from two aspects: theoretical development and applications. After a brief outline of its development and extension over a twenty year period, the ER approach is outlined with a focus on the links among its various developments. Future research directions in the area are also explored in the survey.

SWOT (Strengths, Weaknesses, Opportunities and Threats) analysis is one of the most widely used strategic planning and decision making tool. However, it still has some structural problems such as the lack of prioritization of the alternatives, and too many extractable strategies. In this paper, we suggest a new approach to ranking the strategy alternatives via the SWOT analysis by making use of the Axiomatic Fuzzy Set (AFS) theory to find the best description of the alternatives and use the Evidential Reasoning (ER) approach in the light of AFS to rank the alternatives. The advantages of using the hybrid method (AFS theory and ER approach) to rank the alternatives are that: arbitrary “values in between” of the Expected Utilities are avoided; Decision Makers (DMs) do not need to give subjective judgments to the values of the degree of belief but instead, degrees of belief are directly obtained from the database via AFS theory. A comparison analysis with previous methodologies is made, and it is shown than the proposed hybrid methodology provides a far higher flexibility and effectiveness.

This paper presents the nonlinear interval optimization (NIO) model to solve optimal power system dispatch (OPSD) with uncertain wind power integrated. In this model, not only the average of the dispatching objective, but its deviation are also taken into account. Therefore, the NIO model based on OPSD is formulated as a multi-objective optimization problem. An optimization algorithm, group search optimizer with multiple producers (GSOMP) is applied to obtain Pareto solutions, which show the tradeoff relationship between the average and deviation of the dispatching objective. Then, a decision-making method, the evidential reasoning (ER) approach, is applied to determine the final dispatch solution. Simulation results based on the modified IEEE 30-bus system prove the applicability and effectiveness of the NIO model to deal with the OPSD, considering the integration of the uncertain wind power.

A variable weight approach was proposed to handle the probability deficiency problem in the evidential reasoning (ER) approach. The probability deficiency problem indicated that the inadequate information in the assessment result should be less than that in the input. However, it was proved that under certain circumstances, the ER approach could not solve the probability deficiency problem. The variable weight approach was based on two assumptions: 1) the greater weight should be given to the rule with more adequate information; 2) the greater weight should be given to the rules with less disparate information. Assessment results of two notional case studies show that 1) the probability deficiency problem is solved using the proposed variable weight approach, and 2) the information with less inadequacy and more disparity is provided for the decision makers to help reach a consensus.

Transformer condition monitoring and assessment is crucial as it can be applied to evaluate its maintenance needs. The current trend is to leave critical power transformers in service uninterrupted unless the monitored parameters indicate otherwise. This ensures optimal use of the facility and uninterrupted power supply to consumers. This paper presents the evaluation of the health status of a transformer using the dissolved gas analysis (DGA), oil testing and evidential reasoning criterion. In the analysis, the dissolved gases and oil testing parameters are first normalized and then transformed into fuzzy variables using trapezoid membership function. Then from fuzzy variables, the condition assessment is transformed into a multiple-attribute decision making (MADM) problem under an evidential reasoning (ER) framework. Two case studies are evaluated using actual field data to verify the methodology. Both demonstrate the applicability of the proposed methodology. This emerges as a good logical and systematic way of assessing the maintenance needs of a power transformer using operational data.

Nowadays, making strategic decisions in a sensitive sector such as energy planning that usually requiresallocating huge funds, time, and resources is a difficult task. For instance, prioritizing a set of Renewable EnergySources (RES) is a complex multi-dimensional task that typically involves a range of conflicting criteria featuringdifferent forms of evaluation data in an uncertain decision-making environment. This process is aligned withseveral sources that can be uncertain, including imprecise information, limited domain knowledge from decisionmakers,and failures to provide accurate judgments from experts. In this study, we propose to use the EvidentialReasoning (ER) approach to manage the expanding complexities and uncertainties in RES prioritization problem.The ER approach is employed as a multiple criteria framework to assess the appropriateness regarding the use ofdifferent renewable energy technologies. A case study is provided to illustrate the implementation process. Resultsshow that using the ER approach when assessing the sustainability of different RES under uncertainty allowsproviding robust decisions, which brings out a more accurate, effective, and better-informed decision-making toolto conduct the evaluation process.

Cross-efficiency aggregation in DEA models using the evidential-reasoning approach

Geographic location selection for river bridge construction is one of the most challenging tasks because it has to deal with both numerical data and qualitative information with uncertainty. Organizational self-assessment, ensuring safety, risk measurement, supplier appraisal and many other things depend on a proper Geo-location selection. River bridge site selection is such an example in which multiple attributes; both qualitative and quantitative criteria have to be analyzed for selecting a proper bridge location. This paper demonstrates the application of a novel method named Evidential Reasoning (ER), which is a Multi Criteria Decision Analysis method applied for decision making where many criteria have to be explicitly considered for making decision. ER approach is capable for addressing the uncertainty of multi-criteria decision problem, where there exist factors of both subjective and quantitative nature. It handles uncertainties by using a degree belief structure. The evidential reasoning approach helps in aggregating degree of belief from lower level attributes to higher level attributes. To conduct the experiments of this work, Geo-location selection for The Padma Bridge construction in Bangladesh is taken as research domain and the experiment results and analysis are discussed in this paper.

Requirement assessment and prioritization are the crucial requirement engineering processes in the software development domain. However, software developers face challenges with various types and categories of requirements due to generality, time, and cost. However, Various techniques are proposed for requirement prioritization, such as hierarchical analytic process (AHP), MOSCOW, cumulative voting (CV), bubble sort, and binary search tree (BST). These techniques are still unreliable for prioritizing a considerable number of requirements which takes a long time. Therefore, there is a crucial need for a reliable and consistent approach that helps deal with functional and non-functional requirements assessment and prioritization, such as the Evidential Reasoning (ER) approach. The study findings showed how the requirements assessment and prioritization process improved according to their weights, evaluation grades, and belief degrees. In addition to the decision matrices that lead to high-quality decisions.KeywordsRequirement prioritizationArchitecture assessmentEvidential Reasoning (ER)Multi-criteria decision making (MCDM)

An empirical test of the evidential reasoning approach’s synthesis axioms

Problems in multi-criteria decision making have always been of considerable interest to researchers over the decades, with various techniques and methodologies evolving continuously in this area. The use of fuzzy-set-based approaches has been widely documented in problems where the qualitative or subjective nature of the data, with information ambiguity or data imprecision, is inherent. In recent times, the approach of evidential reasoning, based on the Dempster–Shafer theory which considers uncertainty of a similar nature, has emerged as an alternative. As such, questions have arisen on the merits, and hence the accuracy, of these two kinds of method, i.e. the fuzzy-set-based approach and evidential reasoning, when applied to the considered problem. This paper aims to address some of these questions through a comparative study on the use of fuzzy-set-based methods and evidential reasoning methodology on the condition monitoring of a turbine on a transportation ship. Since the assessment of the ranking problem of the condition-monitoring technique has been well investigated and reported in the literature, methodical comparisons of the results obtained from the fuzzy-set-based methods and the evidential reasoning approach are studied. In the studies, it was concluded that the use of assessment with evidential reasoning, which considers incomplete information, provides identical results with those of fuzzy-set-based methods. The paper also revealed how the use of the evidential reasoning approach could replace fuzzy-set-based methods, which often demands complete subjective information.

This paper reports the development of a multiple-attribute decision-making (MADM) modeling framework and methodology for application to the scoring process against the criteria of the European Foundation for Quality Management (EFQM) Model for Excellence. The MADM model is based on the evidential reasoning approach with the focus of the initial development and application on the people management enabler. The model has been tested with real data from two award application documents from an electricity distribution utility. The scores achieved using the methodology have proven the validity and reliability of the model.

Evidential reasoning(ER) is one of the multi-attribute decision making(MADM) approaches that can cope with uncertainties and ambiguities which may caused by the subjective judgments of decision makers. In a MADM problem, many kinds of indicators are always involved, for instance, profit indicator, cost indicator, deviated indicator, fixed indicator and so on. The present ER approach only considers the first three kinds of indicator, so it limits the application of the ER based assessment approach. In the paper, the ER based MADM approach is extended to deal with fixed indicator. Specifically, we will analyze the frame of discernment of fixed indicator, and the transformation methods from the assessment of fixed indicator to probability assignment on evaluation grades are also given. So we will get the ER approach based on profit indicator, cost indicator and fixed indicator.

Enterprise architecture (EA) frameworks are used to ensure interoperability of information systems and improve the effectiveness and efficiency of business organisations. Several methods have been proposed for selecting suitable frameworks. Although these methods are useful, none of them captures the uncertainties inherent in multi-attribute framework selection problems that embrace both qualitative and quantitative attributes. We propose an evidential reasoning (ER) approach to aggregate subjective and objective judgements associated with qualitative and quantitative attributes rationally and systematically. The ER approach proposed here has the following advantages over other multi-attribute decision-making (MADM) approaches used for EA framework selection: 1) the relative importance of different attributes is incorporated into the model; 2) attribute ratings are treated as assessment grades rather than precise numerical values; 3) attributes can be assessed with belief functions to capture uncertainties. A case study is provided to illustrate the implementation process of the ER approach proposed in this study.