Multiobjective and multiattribute decision making in a fuzzy environment and their power engineering applications

Ekel, Petr; Kokshenev, Illya; Parreiras, Roberta; Pedrycz, Witold; Pereira, Joel

doi:10.1016/j.ins.2016.04.030

Cited by 41 publications

(32 citation statements)

References 45 publications

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“…Dempster-Shafer evidence theory (D-S theory) [1,2] is widely used in many real applications [3][4][5][6][7][8] due to its advantages in handling uncertain information, since decision-relevant information is often uncertain in real systems [9][10][11]. However, in D-S theory, the results with Dempster's combination rule are counterintuitive [12] when the given evidence highly conflict with each other.…”

Section: Introductionmentioning

confidence: 99%

A correlation coefficient for belief functions

Jiang

2018

International Journal of Approximate Reasoning

229

139

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How to manage conflict is still an open issue in Dempster-Shafer evidence theory.The correlation coefficient can be used to measure the similarity of evidence in Dempster-Shafer evidence theory. However, existing correlation coefficients of belief functions have some shortcomings. In this paper, a new correlation coefficient is proposed with many desirable properties. One of its applications is to measure the conflict degree among belief functions. Some numerical examples and comparisons demonstrate the effectiveness of the correlation coefficient.

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Section: Introductionmentioning

confidence: 99%

A correlation coefficient for belief functions

Jiang

2018

International Journal of Approximate Reasoning

229

139

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“…This is based on the use of diverse preference formats acceptable for different DMs, their transformation to fuzzy preference relations (FPRs), and processing within the framework of the   R X , models [8]. This approach has already been widely used to resolve various power engineering problems (for instance, [9] and [10]), and it is utilized in this work, for the first time, for the processing of spatialized information.…”

Section: Introductionmentioning

confidence: 99%

Multiattribute Spatial Decision-Making for Qualitative Information Processing as Applied to the Renewable Energy Generating Sites Prospection

Silva

Ekel

Vieira³

et al. 2020

IEEE Access

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The objective of this research is to develop methodological tools to consider qualitative information in solving spatial problems with their particular application to the determination of generating sites for renewable energy systems. This locational problem has a complex nature and its solution has to be capable to take into account a wide range of considerations, as well as evaluations of a qualitative character. In this sense, the proposed process of elaborating decisions starts with studies related to the spatial criteria, which are modeled and processed within the Spatial Multicriteria Analysis. The relative importance of the spatial criteria is established by the involved experts on the basis of the construction, transformation, and processing of preference relations within the framework of the Analytic Hierarchy Process. The resulting decision maps are generated by aggregating the spatial criteria, applying the Ordered Weighted Averaging operator and the linguistic quantifiers. These maps permit one to highlight rational solution alternatives, which, if necessary, are evaluated, compared, chosen, prioritized, and/or ordered, applying additional spatial criteria (of quantitative as well as qualitative character), by means of techniques for preference modeling in a fuzzy environment (within the framework of so-called   R X , models). The corresponding decision process permits one to take into account and process quantitative and qualitative spatial criteria as well as preferences and judgments of the experts. The paper results are illustrated by a Case Study related to the determination of the most suitable locational alternatives for renewable power plants in the Minas Gerais state, Brazil.

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“…To make matters worse, the presence of multi-constrained problem adds to the unpredictability in the development of complex systems. Thus, it is very difficult to predict the trend of complex systems in a scientific, accurate and reliable manner [3,4]. To overcome the difficulty, the key lies in capturing the constraints, especially when they are fuzzy and uncertain, accurately, and using them to make effective predictions of the trend of complex systems.…”

Section: Introductionmentioning

confidence: 99%

Design of Improved Algorithm and Model for Multi-Constrained Fuzzy Predictive Analysis

Song¹,

Xie²,

Shi³

2019

RIA

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Complex system engineering often has high fuzziness and multiple constraints. In this case, it is difficult to achieve consistent results through predictive analysis. To solve the problem, this paper explores the key techniques and methods for predictive analysis on complex systems, and puts forward an improved strategy for multi-constrained fuzzy predictive analysis. The author explained the normalization, weighting, granularity setting and classic domain of the attributes of multiple constraints, introduced the calculation of the fuzzy distance and fuzzy closeness for the attributes of multiple constraints, and detailed the realization of our algorithm and model multi-constrained fuzzy predictive analysis. The effectiveness and feasibility of our algorithm and model were demonstrated through comparison with relevant data in the literature. The results show that the results of our approach agree with those of the literature. The proposed algorithm and model provide a good theoretical basis to predictive analysis of complex system engineering, especially that with multiple fuzzy constraints.

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Multiobjective and multiattribute decision making in a fuzzy environment and their power engineering applications

Cited by 41 publications

References 45 publications

A correlation coefficient for belief functions

A correlation coefficient for belief functions

Multiattribute Spatial Decision-Making for Qualitative Information Processing as Applied to the Renewable Energy Generating Sites Prospection

Design of Improved Algorithm and Model for Multi-Constrained Fuzzy Predictive Analysis

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