Optimal power system dispatch with wind power integrated using nonlinear interval optimization 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.

It is difficult to make decision when the information is diverse and the weight of attribute is unknown.A weight calculating method based on uncertain entropy was proposed in this paper to solve this problem.In this new method,both the complete and incomplete assessments of every attribute were transformed under unified belief framework,and the objective weight of every attribute could be calculated based on its entropy.With the Evidential Reasoning(ER) approach,the new weight calculating method could realize Multiple Attribute Decision Making(MADM) based on uncertain entropy while the weight of attribute is unknown.A case was provided to illustrate the validity and feasibility of this method.

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.

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

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)

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.

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.

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.

Enhancing fuzzy evidential reasoning approach using dynamic adjustment mechanism and new rule-based transformation for engineering emergency response evaluation

This paper aims at proposing a framework for modelling the safety of an engineering system with various types of uncertainties using a fuzzy rule-based evidential reasoning (FURBER) approach. In the framework, parameters used to define the safety level, including failure rate, failure consequence severity and failure consequence probability, are described using fuzzy linguistic variables; a fuzzy rule-base designed on the basis of a belief structure is used to capture uncertainty and non-linear relationships between these parameters and the safety level; and the inference of the rule-based system is implemented using the evidential reasoning algorithm. A numerical study of collision risk analysis for a kind of offshore platform is used to illustrate the application of the proposed approach.

Attribute weights exert a significant effect on the solution in multi-attribute decision analysis (MADA), since solutions produced by varying attribute weights probably vary. When a decision maker has inadequate valid data, understanding or experience to produce exact attribute weights, he/she perhaps wants to seek a solution with highest reliability, referred to in this study as a highly reliable solution. To this end, a high-reliability evidential reasoning (ER) approach is put forward in the present work, which achieves alternatives comparison through determination of their reliability relative to attribute weights under ER scenario. Initially, the best alternative supported by single or multiple sets of attribute weights was determined. Then, reliability estimation is given for every alternative. In the case of highest reliability, the optimal interval of attribute weights and evaluation grades between the optimal alternative is measured and their ranking is generated. The proposed approach to the process is based on a combination of identifying these alternatives and measuring their reliability. The problem of automobile performance evaluation is explored, finding that the proposed approach is capable of effectively generating high reliability solutions for MADA problems.

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