Fuzzy multi-criteria decision-making method-based operational assessment of Chinese electricity markets

The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and contributes to stable and healthy market growth.This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness.The evaluation framework,comprising nine indicators organized into two tiers,was constructed based on three aspects:market design,market efficiency,and developmental coordination.Furthermore,a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies.This model aimed to ensure both accuracy and comprehensiveness in market operation assessment.Subsequently,empirical analyses were conducted on four typical provincial-level electricity markets in China.The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles.Zhejiang and Shandong ranked second and third,respectively,whereas Sichuan exhibited the poorest market performance.Sichuan’s electricity market must be improved in terms of market design,such that market players can obtain a fairly competitive environment.The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study.Finally,policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.

Fuzzy multi-criteria decision-making approach with incomplete information based on evidential reasoning

The weights of criteria are incompletely known and the criteria values are incomplete and uncertain or even default in some fuzzy multi-criteria decision-making problems.For those problems,an approach based on evidential reasoning is proposed,in which the criteria values are integrated on the basis of analytical algorithm of evidential reasoning,and then nonlinear programming models of each alternative are developed with the incomplete information on weights.The genetic algorithm is employed to solve the models,producing the weights and the utility interval of each alternative,and the ranking of the whole set of alternatives can be attained.Finally,an example shows the effectiveness of the method.

Tri-Intersectional Model of Leadership by Values:A Fuzzy Multi-Criteria Decision Making Open Technology Assessment System

Purpose-Nowadays successful organizations need to be masters at leadership by values to play in a constantly changing and transforming environment.But how can leaders and organizations effectively convene strategic and culture development based on values?This paper presents the Tri-Intersectional Model of Leadership by Values(TMLV)in which leaders and organizations can integrate a sustainable strategy,as well as a culture and value-based management system that simultaneously leverages human,financial,and social resources.With its three essential axes of values(economic-pragmatic,emotional-development,and ethical-social)at their intersection points,it allows leaders to focus on the strategy linkages:innovation-intersection between the economic-pragmatic values axis and the emotional-development values axis-allows them to develop sustainable innovations;survival-intersection between the economic-pragmatic values axis and the ethical-social values axis-enhances their organization’s survival;finally,sensibility-intersection between the economic-pragmatic values axis and the ethical-social values axis-makes them more humane and more socially-responsible.The application of the TMLV,using the Fuzzy Multi-Criteria Decision Making Open Technology Assessment System,can be a great inspiration for stimulating and working with values in organizations,as well as allowing leaders to develop a values-based,high-involvement,and performance-oriented culture.Methodology/Approach-This research considers empirical data analysis of the 37 case studies of the EU-InnovatE project(http//www.euinnovate.com)-a pioneering initiative to align innovation values to integrate the end user into the process of innovation and entrepreneurship related to a sustainable lifestyle and the green economy in Europe-using a fuzzy multiple-criteria decision making method and open technologies system,such as server-side PHP language,MariaDB Database,fork of MYSQL Database Management System,and JavaScript libraries to perform operation directly on the user’s browser.Findings-The application of the TMLV model,considering empirical analysis of the extracted values from the case studies,using the Fuzzy Multi-Criteria Decision Making Open Technology Assessment System,can be a great inspiration for stimulating and working with values in João organizations,as well as allowing leaders to develop suitable strategies and interventions for shaping a sustainable high-performance culture.Research implications-This research can be a starting point for further research to assess the effectiveness of the leadership model based on a decision-making open technology system in any given organization,as well as to invite researchers who have positive passion about working with values to participate in the improvement of this tool.Originality/value-The Tri-Intersectional Model of Leadership by Values using the Fuzzy Multi-Criteria Decision Making Open Technology Assessment System is presented as an evolution in leadership models that may be used to fuel innovation,survival,and a sensibility strategic focus that is necessary to optimize human and organizational performance and deliver effective solutions to the massive array of human,financial,and social problems we face today.

Prospect Theory Based Hesitant Fuzzy Multi-Criteria Decision Making for Low Sulphur Fuel of Maritime Transportation

The environmental impact of maritime transport has now become a relevant issue in sustainable policy formulation and has attracted increasing interest from academia.For the sustainable development of maritime transport,International Maritime Organization stipulates that the sulfur content of ship emissions will reach 0.5 from 2020.With the approaching of the stipulated implementation date,shipowners need to adopt scientific methods to make decision on low sulfur fuel.In this study,we applied a prospect theory based hesitant fuzzy multi-criteria decision-making model to obtain the optimal decision of low Sulphur marine fuel.For this purpose,the hesitant fuzzy decision matrix is established to collect expert opinions,the maximizing deviation method is adopted to determine criteria weights.According to calculate the Euclidean distance from the reference points,we obtain the comprehensive prospect values of alternatives.Lastly,a case study is carried out to illustrate the significance and effectiveness of the proposed methodology.The innovation of this study is that it is the first-time adopting prospect theory and hesitate fuzzy sets to multi-criteria decision making for low Sulphur marine fuel,which provides an effective decision model for shipping companies under Low Sulphur regulations,and can also be extended to other industries.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

Method for triangular fuzzy multiple attribute decision making based on two-dimensional density operator method

Aiming at the triangular fuzzy(TF)multi-attribute decision making(MADM)problem with a preference for the distribution density of attribute(DDA),a decision making method with TF number two-dimensional density(TFTD)operator is proposed based on the density operator theory for the decision maker(DM).Firstly,a simple TF vector clustering method is proposed,which considers the feature of TF number and the geometric distance of vectors.Secondly,the least deviation sum of squares method is used in the program model to obtain the density weight vector.Then,two TFTD operators are defined,and the MADM method based on the TFTD operator is proposed.Finally,a numerical example is given to illustrate the superiority of this method,which can not only solve the TF MADM problem with a preference for the DDA but also help the DM make an overall comparison.

The Spherical q-Linear Diophantine Fuzzy Multiple-Criteria Group Decision-Making Based on Differential Measure

Spherical q-linearDiophantine fuzzy sets(Sq-LDFSs)provedmore effective for handling uncertainty and vagueness in multi-criteria decision-making(MADM).It does not only cover the data in two variable parameters but is also beneficial for three parametric data.By Pythagorean fuzzy sets,the difference is calculated only between two parameters(membership and non-membership).According to human thoughts,fuzzy data can be found in three parameters(membership uncertainty,and non-membership).So,to make a compromise decision,comparing Sq-LDFSs is essential.Existing measures of different fuzzy sets do,however,can have several flaws that can lead to counterintuitive results.For instance,they treat any increase or decrease in the membership degree as the same as the non-membership degree because the uncertainty does not change,even though each parameter has a different implication.In the Sq-LDFSs comparison,this research develops the differentialmeasure(DFM).Themain goal of the DFM is to cover the unfair arguments that come from treating different types of FSs opposing criteria equally.Due to their relative positions in the attribute space and the similarity of their membership and non-membership degrees,two Sq-LDFSs formthis preference connectionwhen the uncertainty remains same in both sets.According to the degree of superiority or inferiority,two Sq-LDFSs are shown as identical,equivalent,superior,or inferior over one another.The suggested DFM’s fundamental characteristics are provided.Based on the newly developed DFM,a unique approach tomultiple criterion group decision-making is offered.Our suggestedmethod verifies the novel way of calculating the expert weights for Sq-LDFSS as in PFSs.Our proposed technique in three parameters is applied to evaluate solid-state drives and choose the optimum photovoltaic cell in two applications by taking uncertainty parameter zero.The method’s applicability and validity shown by the findings are contrasted with those obtained using various other existing approaches.To assess its stability and usefulness,a sensitivity analysis is done.

Aggregation operators on intuitionistic trapezoidal fuzzy number and its application to multi-criteria decision making problems

Intuitionistic trapezoidal fuzzy numbers and their operational laws are defined. Based on these operational laws, some aggregation operators, including intuitionistic trapezoidal fuzzy weighted arithmetic averaging operator and weighted geometric averaging operator are proposed. Expected values, score function, and accuracy function of intuitionitsic trapezoidal fuzzy numbers are defined. Based on these, a kind of intuitionistic trapezoidal fuzzy multi-criteria decision making method is proposed. By using these aggregation operators, criteria values are aggregated and integrated intuitionistic trapezoidal fuzzy numbers of alternatives are attained. By comparing score function and accuracy function values of integrated fuzzy numbers, a ranking of the whole alternative set can be attained. An example is given to show the feasibility and availability of the method.

Multi-criteria group decision making with fuzzy data:an extension of the VIKOR method

The VIKOR method is a multi-criteria decision making aid, which employs linear normalization to offer compromise solu- tions and has been successfully applied to various group decision making problems. However, the conventional VIKOR techniques used to integrate group judgments and the information loss arising from defuzzification are problematic and distort final outcomes. An improved integration method, which is optimization-based, is proposed. And it can handle fuzzy criteria values and weights. The precondition for accurately defuzzifying triangular fuzzy num- bers is identified. Several effective defuzzification procedures are proposed to improve the extant VIKOR, and a comprehensive evaluation framework is offered to aid multi-criteria group decision making. Finally, a numerical example is provided to illustrate the practicability of the proposed method.

Multi-Criteria Fuzzy-Based Decision Making Algorithm to Optimize the VHO Performance in Hetnets

Despite the seemingly exponential growth of mobile and wireless communication,this same technology aims to offer uninterrupted access to different wireless systems like Radio Communication,Bluetooth,and Wi-Fi to achieve better network connection which in turn gives the best quality of service(QoS).Many analysts have established many handover decision systems(HDS)to enable assured continuous mobility between various radio access technologies.Unbrokenmobility is one of themost significant problems considered in wireless communication networks.Each application needs a distinct QoS,so the network choice may shift appropriately.To achieve this objective and to choose the finest networks,it is important to select a best decision making algorithm that chooses the most effective network for every application that the user requires,dependent on QoS measures.Therefore,the main goal of the proposed system is to provide an enhanced vertical handover(VHO)decision making programby using aMulti-CriteriaFuzzy-Based algorithm to choose the best network.Enhanced Multi-Criteria algorithms and a Fuzzy-Based algorithm is implemented successfully for optimal network selection and also to minimize the probability of false handover.Furthermore,a double packet buffer is utilized to decrease the packet loss by 1.5%and to reduce the number of handovers up to 50%compared to the existing systems.In addition,the network setup has an optimized mobilitymanagement system to supervise the movement of the mobile nodes.

Multiple attribute decision making method based on trapezoid fuzzy linguistic variables

The problem of multiple attribute decision making under fuzzy linguistic environments, in which decision makers can only provide their preferences （attribute values）in the form of trapezoid fuzzy linguistic variables（TFLV）, is studied. The formula of the degree of possibility between two TFLVs is defined, and some of its characteristics are studied. Based on the degree of possibility of fuzzy linguistic variables, an approach to ranking the decision alternatives in multiple attribute decision making with TFLV is developed. The trapezoid fuzzy linguistic weighted averaging （TFLWA） operator method is utilized to aggregate the decision information, and then all the alternatives are ranked by comparing the degree of possibility of TFLV. The method can carry out linguistic computation processes easily without loss of linguistic information, and thus makes the decision results reasonable and effective. Finally, the implementation process of the proposed method is illustrated and analyzed by a practical example.

Enabling weakened hedges in linguistic multi-criteria decision making

A semantics-based model is proposed to enable weakened hedges, such as ＂more or less＂ and ＂roughly＂ in the context of linguistic multi-criteria decision making. First, the resemblance relations are defined based on the semantics of terms on the domain. Then, the hedges can be represented after the upper and loose upper approximations of a linguistic term are derived. Accordingly, some compact formulae can be derived for the semantics of linguistic expressions with hedges. Parameters in these formulae are objectively determined according to the semantics of original terms. The proposed model presents a more natural way to express the decision information under uncertainties and its semantics is clear. The proposed model is clarified by solving the problem of evaluation and selection of sustainable innovative energy technologies. Computational results demonstrate that the model can deal with various uncertainties of the problem. Finally, the model is compared with existing techniques and extended to the case when the semantics of terms are represented by trapezoidal fuzzy numbers.

Land Evaluation Method Based on Decision Tree Produced by C4.5 and Fuzzy Decision

[Objective]The aim was to overcome the shortage of being difficult to build land evaluation model when the impact factors had continuous value in the traditional land evaluation process,as well as to improve the intelligibility of the land evaluation knowledge.[Method] The land evaluation method combining classification rule extracted by C4.5 algorithm with fuzzy decision was proposed in this study.[Result] The result of Second General Soil Survey of Guangdong Province had demonstrated that the method was convenient to extract classification rules,and by using only 100 rules,quantity correct rate 86.67% and area correct rate 84.80% of land evaluation could be obtained.[Conclusions] The use of C4.5 algorithm to obtain the rules,combined with fuzzy decision algorithm to build classifiers had got satisfactory results,which provided a practical algorithm for the land evaluation.

Fuzzy data envelopment analysis approach based on sample decision making units

The conventional data envelopment analysis （DEA） measures the relative efficiencies of a set of decision making units with exact values of inputs and outputs. In real-world prob- lems, however, inputs and outputs typically have some levels of fuzziness. To analyze a decision making unit （DMU） with fuzzy input/output data, previous studies provided the fuzzy DEA model and proposed an associated evaluating approach. Nonetheless, numerous deficiencies must still be improved, including the α- cut approaches, types of fuzzy numbers, and ranking techniques. Moreover, a fuzzy sample DMU still cannot be evaluated for the Fuzzy DEA model. Therefore, this paper proposes a fuzzy DEA model based on sample decision making unit （FSDEA）. Five eval- uation approaches and the related algorithm and ranking methods are provided to test the fuzzy sample DMU of the FSDEA model. A numerical experiment is used to demonstrate and compare the results with those obtained using alternative approaches.

Extension of TOPSIS for fuzzy multi-attribute decision making problem based on experimental analysis

This paper is concerned with a technique for order performance by similarity to ideal solution（TOPSIS） method for fuzzy multi-attribute decision making,in which the information about attribute weights is partly known and the attribute values take form of triangular fuzzy numbers.Considering the fact that the triangular fuzzy TOPSIS results yielded by different distance measures are different from others,a comparative analysis of triangular fuzzy TOPSIS ranking from each distance measure is illustrated with discussion on standard deviation.By applying the most reasonable distance,the deviation degrees between attribute values are measured.A linear programming model based on the maximal deviation of weighted attribute values is established to obtain the attribute weights.Therefore,alternatives are ranked by using TOPSIS method.Finally,a numerical example is given to show the feasibility and effectiveness of the method.

Novel combinatorial algorithm for the problems of fuzzy grey multi-attribute group decision making

To study the fuzzy and grey information in the problems of multi-attribute group decision making, the basic concepts of both fuzzy grey numbers and grey interval numbers are given firstly, then a new model of fuzzy grey multi-attribute group decision making based on the theories of fuzzy mathematics and grey system is presented. Furthermore, the grey interval relative degree and deviation degree is defined, and both the optimistic algorithm of the grey interval relational degree and the algorithm of deviation degree minimization for solving this new model are also given. Finally, a decision making example to demonstrate the feasibility and rationality of this new method is given, and the results by using these two algorithms are uniform.

Potential landfill site selection for solid waste disposal using GIS and multi-criteria decision analysis(MCDA)

Proper solid waste disposal is an important socioeconomic concern for all developing countries.Municipalities have their own policies,individual approaches and methods to manage the solid wastes.They consider wastelands outside the urban area as the best suitable for the solid waste disposal.Such improper site selection will create morphological changes that lead to environmental hazards in the urban and its surrounding areas.In this research,the site selection for urban solid waste disposal in the Coimbatore district used geographical information system(GIS)and multi-criteria decision analysis(MCDA).Thematic layers of lineament density,landuse/landcover,population density,groundwater depth,drainage density,slope,soil texture,geology and geomorphology were considered as primary criteria and weights for criteria,and sub-criteria were assigned by MCDA analysis.The resultant weight score was validated by consistency ratio so that the efficiency of the selected criteria was justified.The overlay analysis in GIS environment provides 17 potential zones in Coimbatore district,among which,four suitable sites were screened and refined with the help of field investigation and visual interpretation of satellite image.The result of landfill suitability map shows the effectiveness of the proposed method.

Entropy-based procedures for intuitionistic fuzzy multiple attribute decision making

The class of multiple attribute decision making （MADM） problems is studied, where the attribute values are intuitionistic fuzzy numbers, and the information about attribute weights is completely unknown. A score function is first used to calculate the score of each attribute value and a score matrix is constructed, and then it is transformed into a normalized score matrix. Based on the normalized score matrix, an entropy-based procedure is proposed to derive attribute weights. Furthermore, the additive weighted averaging operator is utilized to fuse all the normalized scores into the overall scores of alternatives, by which the ranking of all the given alternatives is obtained. This paper is concluded by extending the above results to interval-valued intuitionistic fuzzy set theory, and an illustrative example is also provided.

Fuzzy Comprehensive Evaluation for Decision Making of Water Saving Irrigation System

A model of fuzzy comprehensive evaluation for water saving irrigation system (WSIS) decision making is proposed based on establishing an index system affected by six kinds of basic factors including qualitative and quantitative indexes. The object function of WSIS is set up by using the concept of fuzzy membership degree, it is to transform characteristic vector matrix into unify membership matrix and extending the least square method to the least of weighted distance square. The optimum weighted membership degree and the inferior weighted membership degree are used to solve the object function. This method effective solves the problem of classify for fuzzy attributive indexes and the problem of optimum for the set of different attributive indexes. A case study shows that the fuzzy comprehensive evaluation model is reasonable and effective in decision making for water saving irrigation system planning.