Performance Evaluation for Sustainability of Strong Smart Grid by Using Stochastic AHP and Fuzzy TOPSIS Methods

Zhao, Huiru; Li, Nana

doi:10.3390/su8020129

Cited by 49 publications

(31 citation statements)

References 38 publications

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“…In real managements, especially for MADM, the attributes are often uncertain information, which is typically characterized by fuzzy information or stochastic information [32][33][34][35]. Of course, the most complex situation is with fuzzy information and stochastic information, simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Multiple Attribute Decision-Making Methods Based on Normal Intuitionistic Fuzzy Interaction Aggregation Operators

Лю¹

2017

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Normal intuitionistic fuzzy numbers (NIFNs), which combine the normal fuzzy number (NFN) with intuitionistic number, can easily express the stochastic fuzzy information existing in real decision making, and power-average (PA) operator can consider the relationships of different attributes by assigned weighting vectors which depend upon the input arguments. In this paper, we extended PA operator to process the NIFNs. Firstly, we defined some basic operational rules of NIFNs by considering the interaction operations of intuitionistic fuzzy sets (IFSs), established the distance between two NIFNs, and introduced the comparison method of NIFNs. Then, we proposed some new aggregation operators, including normal intuitionistic fuzzy weighted interaction averaging (NIFWIA) operator, normal intuitionistic fuzzy power interaction averaging (NIFPIA) operator, normal intuitionistic fuzzy weighted power interaction averaging (NIFWPIA) operator, normal intuitionistic fuzzy generalized power interaction averaging (NIFGPIA) operator, and normal intuitionistic fuzzy generalized weighted power interaction averaging (NIFGWPIA) operator, and studied some properties and some special cases of them. Based on these operators, we developed a decision approach for multiple attribute decision-making (MADM) problems with NIFNs. The significant characteristics of the proposed method are that: (1) it is easier to describe the uncertain information than the existing fuzzy sets and stochastic variables; (2) it used the interaction operations in part of IFSs which could overcome the existing weaknesses in operational rules of NIFNs; (3) it adopted PA operator which could relieve the influence of unreasonable data given by biased decision makers; and (4) it made the decision-making results more flexible and reliable because it was with generalized parameter which could be regard as the risk attitude value of decision makers. Finally, an illustrative example is given to verify its feasibility, and to compare with the existing methods.Keywords: power average (PA); normal intuitionistic fuzzy number (NIFN); normal intuitionistic fuzzy power interaction averaging (NIFPIA) operator; interaction operations; multiple attribute decision-making (MADM)

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Section: Introductionmentioning

confidence: 99%

Multiple Attribute Decision-Making Methods Based on Normal Intuitionistic Fuzzy Interaction Aggregation Operators

Лю¹

2017

Symmetry

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“…There are a number of approaches for determining indicator weighting values, such as weighting assumption [54], the Analytic Hierarchy Process (AHP) [55], Delphi [56], and the Entropy method [57]. Among these methods, the Entropy method has been found effective in setting up weightings between a group of indicators.…”

Section: Establishing Weighting Values Between Indicatorsmentioning

confidence: 99%

A Global Perspective on the Sustainable Performance of Urbanization

et al. 2016

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Urbanization, particularly in developing countries, is a major strategy for development. However, major concerns accompany it, such as air pollution, habitat destruction, and loss of arable land. In responding to these challenges, governments throughout the world have been implementing various policy mechanisms to guide the practice of urbanization towards sustainable development. It appears that there is little research investigating the outcomes of those efforts in implementing sustainable urbanization strategies. This paper provides a profile of sustainable urbanization from a global perspective. Data used for this research cover 111 countries and are collected from the World Bank database and the United Nation database. A ranking list of sustainable performance of urbanization between these countries is produced and discussed. The study suggests that countries at different stages of urbanization have achieved different levels of sustainable performance. The research results provide significant references for future study in the field of urbanization from a global perspective.

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“…Cavallaro [12] used Takagi-Sugeno fuzzy inference in developing a synthetic index for the sustainability assessment of production of the biomass for energy purposes. Zhao and Li [13] proposed a hybrid framework for the evaluation of the Strong Smart Grid (SSG) performance using AHP and technique for order of preference by similarity to ideal solution (TOPSIS). Phillis et al [14] developed SAFE (sustainability assessment by fuzzy evaluation), which is a measuring scheme of sustainability as well as a decision support tool for policymaking that uses the fuzzy rule-based system to measure the overall sustainability of a country.…”

Section: Introductionmentioning

confidence: 99%

Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique

et al. 2018

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Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this technique has been rarely explored when developing sustainability assessment methods. In addition, most of the methods developed in the literature solely rely on fuzzy logic. The main shortcoming of solely using the fuzzy logic rule-based technique is that it cannot automatically learn from the data. This problem of fuzzy logic has been solved by the use of neural networks in many real-world problems. The combination of these two techniques will take the advantages of both to precisely predict the output of a system. In addition, combining the outputs of several predictors can result in an improved accuracy in complex systems. This study accordingly aims to propose an accurate method for measuring countries' sustainability performance using a set of real-world data of the sustainability indicators. The adaptive neuro-fuzzy inference system (ANFIS) technique was used for discovering the fuzzy rules from data from 128 countries, and ensemble learning was used for measuring the countries' sustainability performance. The proposed method aims to provide the country rankings in term of sustainability. The results of this research show that the method has potential to be effectively implemented as a decision-making tool for measuring countries' sustainability performance.

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Performance Evaluation for Sustainability of Strong Smart Grid by Using Stochastic AHP and Fuzzy TOPSIS Methods

Cited by 49 publications

References 38 publications

Multiple Attribute Decision-Making Methods Based on Normal Intuitionistic Fuzzy Interaction Aggregation Operators

Multiple Attribute Decision-Making Methods Based on Normal Intuitionistic Fuzzy Interaction Aggregation Operators

A Global Perspective on the Sustainable Performance of Urbanization

Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique

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