Multiple Attribute Group Decision-Making Methods with Completely Unknown Weights in Intuitionistic Fuzzy Setting and Interval-Valued Intuitionistic Fuzzy Setting

The existing multiple attribute group decision-making approaches based on intuitionistic fuzzy sets (IFSs) or interval-valued intuitionistic fuzzy sets (IVIFSs) are considered as the situation that the weights of experts are given beforehand and the attribute weights are known or unknown. To better describe the uncertain decision environment and solve the corresponding decision problem, multiple attribute group decision-making methods with completely unknown weights of both experts and attributes are proposed in intuitionistic fuzzy setting and interval-valued intuitionistic fuzzy setting. Entropy weight models can be used to determine the weights of both experts and attributes from intuitionistic fuzzy decision matrices or interval-valued intuitionistic fuzzy decision matrices, and then the evaluation formulas of weighted correlation coefficients between alternatives and the ideal alternative are introduced in intuitionistic fuzzy setting and interval-valued intuitionistic fuzzy setting. The alternatives can be ranked and the most desirable one(s) can be selected according to the values of the weighted correlation coefficients for IFSs or IVIFSs. Finally, two numerical examples demonstrate the effectiveness of the proposed methods: they are capable for handling the multiple attribute group decision-making problems with completely unknown weights.     

An Extended PROMETHE II Multi-Criteria Group Decision Making Technique Based on Intuitionistic Fuzzy Logic for Sustainable Energy Planning

The implementations of Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) category to complex multi-criteria group decision making (MCGDM) scenarios have been included in thousands areas. Outranking methods such as PROMETHEE II are also greatly employed in energy planning application. In MCGDM methods if decision makers (DMs) are not able to treat precise data in order to define their preferences, the intuitionistic fuzzy set (IFS) theory enables them. IFS attributes are connected with the degree of membership and non-membership, and can be used to draw uncertainty in group decision-making situations. In this paper, a new version of the PROMETHEE II method is proposed, aiming at solving MCGDM problems. Linguistic variables are expressed in the membership function and non-membership function of IFS which are used to assess the weights of all criteria and the ratings of each alternative with respect to each criteria. Conditional normalized Euclidean distance measure is adopted to measure deviations between alternatives on intuitionistic fuzzy set. Then, a ranking algorithm is applied to indicate the order of superiority of alternatives. Finally, a practical example is given to an application of sustainable energy planning to verify our proposed method. Additionally, a comparative analysis is done among the proposed PROMETHEE II method and the intuitionistic fuzzy technique for order preference by similarity to ideal solution (IF-TOPSIS) method and elimination and choice translating reality method (IF-ELECTRE).     

基于改进多属性群决策的国有企业经营者选择研究

国有企业经营者选择机制与方法的转变是国有企业改革的重点之一,针对经营者选择的研究多集中在定性分析。在经营者能力信号传递研究基础上,引入针对企业自身特点的能力评价指标,通过权重模糊差异对能力评价指标权重进行修正,利用改进多属性群决策方法对候选经营者进行能力评估与选择,并通过一个实例演示该方法的决策过程。     

A Case Study of Group Decision Method for Environmental Foresight and Water Resources Planning Using a Fuzzy Approach

Group decision-making (i.e., multi-expert) is an important subject in planning water resources. The inherent complexity and uncertainty of real world urge many experts to be involved in decision-making processes. This paper presents an application of the linguistic-label aggregation method in a real-life case study. The case was taken from a foresight exercise in Colombia (South America) concerning environmental and water resources planning in a river basin. The group decision-making problem is solved using a four-step approach based on (i) the evaluation of experts’ opinions, (ii) the aggregation of opinions for each alternative, (iii) fuzzy ranking, and (iv) final assessment. Two main issues that are new in our work is that we consider temporal linguistic labels and a fuzzy ranking procedure that is able to include the mean, the standard deviation, the fuzzy membership function and the frequency of experts’ opinions for each alternative. The approach is developed and implemented on a computational tool. Results show an efficient decision-making process, that is, the tool demonstrated to deal with shortest time frames and to increase the efficiency of the planning resources, mainly because allows the decision manager to focus on the establishment of criteria. The latter also leads to objectivity and eases the identification and discussion of elements of consensus in decisions that otherwise may be embedded in individuals’ interests.     

Robust Multi-criteria Service Composition in Information Systems

Service compositions are used to implement business processes in a variety of application domains. A quality of service (QoS)-aware selection of the service to be composed involves multiple, usually conflicting and possibly uncertain QoS attributes. A multi-criteria solution approach is desired to generate a set of alternative service selections. In addition, the uncertainty of QoS-attributes is neglected in existing solution approaches. Hence, the need for service reconfigurations is imposed to avoid the violation of QoS restrictions. The researched problem is NP-hard. This article presents a heuristic multi-criteria service selection approach that is designed to determine a Pareto frontier of alternative service selections in a reasonable amount of time. Taking into account the uncertainty of response times, the obtained service selections are robust with respect to the constrained execution time. The proposed solution approach is based on the Non-dominated Sorting Genetic Algorithm (NSGA)-II extended by heuristics that exploit problem specific characteristics of the QoS-aware service selection. The applicability of the solution approach is demonstrated by a simulation study.     

A risk-based decision support framework for selection of appropriate safety measure system for underground coal mines

In the context of underground coal mining industry, the increased economic issues regarding implementation of additional safety measure systems, along with growing public awareness to ensure high level of workers safety, have put great pressure on the managers towards finding the best solution to ensure safe as well as economically viable alternative selection. Risk-based decision support system plays an important role in finding such solutions amongst candidate alternatives with respect to multiple decision criteria. Therefore, in this paper, a unified risk-based decision-making methodology has been proposed for selecting an appropriate safety measure system in relation to an underground coal mining industry with respect to multiple risk criteria such as financial risk, operating risk, and maintenance risk. The proposed methodology uses interval-valued fuzzy set theory for modelling vagueness and subjectivity in the estimates of fuzzy risk ratings for making appropriate decision. The methodology is based on the aggregative fuzzy risk analysis and multi-criteria decision making. The selection decisions are made within the context of understanding the total integrated risk that is likely to incur while adapting the particular safety system alternative. Effectiveness of the proposed methodology has been validated through a real-time case study. The result in the context of final priority ranking is seemed fairly consistent.     

An Integrated Group Decision-Making Method Dealing with Fuzzy Preferences for Alternatives and Individual Judgments for Selection Criteria

Organizations often require decisions to be made by a group, and decision makers often have fuzzy preferences for alternatives and individual judgments when attempting to reach an optimal solution. In order to deal with the fuzziness of preference of decision makers, this paper proposes an integrated fuzzy group decision-making method. This method allows group members to express fuzzy preferences for alternatives and individual judgments for solution selection criteria. It also allowed for the weighting of group members. The method then aggregates these elements into a compromise group decision which is the most acceptable for the group as a whole. This method has been implemented and tested. An example is presented to illustrate the method.     

A Sample Survey Based Linguistic MADM Method with Prospect Theory for Online Shopping Problems

In MADM problems, the attributes are often rated in linguistic variables, some researchers transform them into numerical values through some formulas. However, it might be inconsistent with real human thinking in some extent. In order to deal with such problems, a sample survey based MADM method with prospect theory is proposed. Firstly, through sampling survey, we collect the data in single point format corresponding to the words, and establish the codebook by mapping words into fuzzy sets, after that fuzzy operation rules are suitable for them. Secondly, based on the reference points, the gain and/or loss matrix is obtained, in accordance with the value function, the prospect value matrix is constructed. Finally, if the attributes are independent, the weighted prospect value of each alternative is computed, if the attributes are dependent, the Choquet integral based prospect value of each alternative is computed. The alternatives are ranked in descending order respect to the defuzzified values. The first alternative is chosen as the best decision result. The feasibility of the proposed method is illustrated through an application in online shopping problem from real life.     

Multiple Attribute Group Decision Making Method Based on Utility Theory Under Interval-Valued Intuitionistic Fuzzy Environment

In this paper, a kind of multiple attribute group decision making problem is studied, where there is no original information about the weights of importance of the attributes and the decision makers (DMs), and the attribute values are given in the form of interval-valued intuitionistic fuzzy numbers (IVIFNs). To solve this problem, a new method is proposed based on utility theory. In the proposed method, the weights of importance of the DMs and the attributes are all determined by using the intuitionistic indexes of related IVIFNs. And then, the alternatives are compared by using their composite interval indexes which are generated based on utility theory. Finally, two numerical examples are proposed to demonstrate the effectiveness of the proposed method.     

Multicriteria Group Decision-Making Method Using Vector Similarity Measures For Trapezoidal Intuitionistic Fuzzy Numbers

Based on the extension of the Jaccard, Dice, and cosine similarity measures, three vector similarity measures between trapezoidal intuitionistic fuzzy numbers (TIFNs) are proposed in the vector space and are applied to the fuzzy multicriteria group decision-making problem, in which the criteria weights and the evaluated values in decision matrix are expressed by TIFNs. Through the weighted similarity measures between each alternative and the ideal alternative, the ranking order of all the alternatives can be determined and the best one(s) can be easily identified as well. A practical example of the developed approaches is given to select the investment alternatives. The decision results of different similarity measures demonstrate that the three similarity measures have better similarity identification. The illustrative example shows that the proposed methods are applicable.     

Conflicting Bifuzzy Multi-attribute Group Decision Making Model with Application to Flood Control Project

We propose a group decision making model based on conflicting bifuzzy sets (CBFS) where evaluation are bi-valued in accordance to the subjective assessment obtained from the experts for the positive and negative views. This paper discusses the weighting methods for particular attribute and subattribute with emphasis given to the unification of subjective and objective weights. The integration of CBFS in the model is naturally done by extending the fuzzy evaluation in parallel with the intuitionistic fuzzy. We introduce a new technique to compute the similarity measure, being the degree of agreement between the experts. We end up the paper by demonstrating the applicability of the proposed model to the empirical case of flood control project, one of the project selection problems.     

Understanding the mechanism and determinants of compromise effects

In this article, compromise effects are distinguished theoretically from attraction effects, which have received more research attention in the past. This article proposes and tests expected‐loss minimization under decision uncertainty as the underlying mechanism to explain compromise effects. Next, it examines the effects of a consumer's familiarity with the product and attribute‐importance structure on compromise effects. The empirical results demonstrate the negative influences of familiarity and asymmetry of attribute importance on compromise effects, as well as the mediating effect of attribute importance on the familiarity–compromise‐effect relationship. Further research directions are also discussed. © 2005 Wiley Periodicals, Inc.     

Application of Fuzzy Risk Analysis for Selecting Critical Processes in Implementation of SPC with a Case Study

Fuzzy risk analysis is widely used in risk assessment of components by linguistic terms. Fuzzy numbers are used to quantify the associated uncertainty. This study employs fuzzy risk analysis to evaluate processes for implementing statistical process control (SPC) in a specified manufacturing system. To reach this goal, fuzzy risk analysis has been applied based on both ranking and similarity of generalized trapezoidal fuzzy numbers in a stepwise procedure. Therefore, a new approach has been introduced for fuzzy risk analysis of processes to overcome the shortcomings of previous fuzzy risk analysis approaches. As a result, fuzzy risk analysis is used as a decision making technique to select critical processes under uncertainty. Also, the application of the proposed SPC implementation algorithm is illustrated in the manufacturing line of a car battery factory.     

A Deviation-Based Approach to Intuitionistic Fuzzy Multiple Attribute Group Decision Making

The aim of this article is to investigate the approach to multiple attribute group decision making (MAGDM) with intuitionistic fuzzy information. We first introduce a deviation measure between two intuitionistic fuzzy numbers, and then utilize the intuitionistic fuzzy hybrid aggregation operator to aggregate all individual intuitionistic fuzzy decision matrices into a collective intuitionistic fuzzy decision matrix. Based on the deviation measure, we develop an optimization model by which a straightforward formula for deriving attribute weights can be obtained. Furthermore, based on the intuitionistic fuzzy weighted averaging operator and information theory, we utilize the score function and accuracy function to give an approach to ranking the given alternatives and then selecting the most desirable one(s). In addition, we extend the above results to MAGDM with interval-valued intuitionistic fuzzy information.     

A within-attribute model of variety-seeking behavior

Existing models view variety seeking as the result of differences in the level of attribute satiation across attributes. An alternative within-attribute variety-seeking (WAVS) model is proposed. The model posits that variety seeking occurs among the nested features, or meaningful value ranges, of an underlying dimension. The resulting pattern of consumption is represented as an oscillation about a consumer's ideal point on the dimension. An empirical study that illustrates different oscillation patterns is reported.     

Interval-Valued Intuitionistic Fuzzy WASPAS Method: Application in Reservoir Flood Control Management Policy

Interval-valued intuitionistic fuzzy sets (IVIFSs) are very flexible tool to cope with the uncertainty arises in multi-criteria decision making (MCDM) problems. In recent times, MCDM problems with interval-valued intuitionistic fuzzy information have achieved more attention from researchers in different areas and consequently, several MCDM methods have been extended for IVIFSs. In this paper, a novel approach based on WASPAS method is developed under IVIFSs. The developed method is based on the operators of IVIFSs, some amendments in the classical WASPAS method and a new process for calculation of criteria and decision experts’ weights. In process for calculating weights, new procedures is propoesd to compute the decision experts’ weights and criteria weights based on interval-valued intuitionistic fuzzy information measures (entropy, divergence and similarity measures) to achieve more realistic weights. Innovative information measures are developed based on the exponential function for IVIFSs to determine the weights of the criteria and decision experts. Since the uncertainty is an unavoidable feature of MCDM problems, the developed method can be a constructive tool for decision-making in an uncertain environment. Further, an uncertain decision making problem of reservoir flood control management policy is implemented with interval-valued intuitionistic fuzzy information, which reveals the effectiveness and reliability of the proposed IVIF-WASPAS method. To validate the result, comparative analysis with existing methods and sensitivity analysis are presented under interval-valued intuitionistic fuzzy environment.     

基于模糊多属性决策的国有企业拍卖机制设计

国有企业拍卖决策机制属于模糊多属性、多轮次决策范畴,运用模糊多属性决策分析工具与拍卖理论相结合的研究思路,设计了国有企业的拍卖规程。研究认为,国有企业所有人虚位及其叠加式代理的特征使国有资产管理人或经理人在并购谈判中容易诱发寻租,以拍卖成交价格为主体的多属性拍卖是国有资产安全、有效的交易方式。同时,在拍卖方式选择上第二价格密封拍卖方式较为适当,最利于实现国有企业拍卖绩效,而通常采用的升价拍卖方式应该慎用。     

Fuzzy Development of Multiple Response Optimization

This paper proposes a developed approach to Multiple Response Optimization (MRO) in two categories; responses without replicates and with some replicates based on fuzzy concepts. At first, the problem without any replicate in responses is investigated, and a fuzzy Decision Support System (DSS) is proposed based on Fuzzy Inference System (FIS) for MRO. The proposed methodology provides a fuzzy approach considering uncertainty in decision making environment. After calculating desirability of each response, total desirability of each experiment is measured by using values of each response desirability, applying membership function and fuzzy rules expressed by experts. Then Response Surface Methodology (RSM) is applied to fit a regression model between total desirability and controllable factors and optimize them. Next, a methodology is proposed for MRO with some replicates in responses which optimizes mean and variance simultaneously by applying fuzzy concepts. After introducing Deviation function based on robustness concept and using desirability function, a two objective problem is constituted. At last, a fuzzy programming is expressed to solve the problem applying degree of satisfaction from each objective. Then the problem is converted to a single objective model with the goals of increasing desirability and robustness simultaneously. The obtained optimum factor levels are fuzzy numbers so that a bigger satisfactory region could be provided. Finally, two numerical examples are expressed to illustrate the proposed methodologies for multiple responses without replicates and with some replicates.     

Sensitivity of multiattribute decision models to alternative specifications of weighting parameters

The issue of attribute weighting in multiattribute decision models is examined. Results are presented which show that the outputs produced by linear multiattribute models are extremely robust with respect to alternative specifications of the weighting parameters unless the number of attributes included in the models is small, the average correlation among the attributes is low, and the dispersion of the weights is large relative to their mean. Implications of these results are discussed for three different types of weighting schemes-regression weighting, equal weighting, and subjective weighting—which are used in multiattribute decision modeling.     

How important is the strategic order of product attribute presentation in the non-life insurance market?

Sales management plays an important role in firms' profit. Its main goal is to determine the best time to present insurance customers with prices, insurers, bundling strategies, and the intermediary's recommendation.In this study, a triangular approach was used. For attribute selection, three focus groups were performed with insurance customers and intermediaries. Conjoint analysis was carried out by presenting the attributes in three different orders.Primacy and recency effects were detected; a transfer or anchor effect was also found related to the importance of the attributes preceding and succeeding a given attribute.According to the findings, salespeople can improve their approach to customers by decreasing the importance given to price and increasing the positive impact of bundling strategies and the intermediary's recommendation in sales.Although the order of attribute presentation has previously been analyzed, this is the first study to examine this issue in non-life insurance products, providing useful information to insurance salespeople and marketing managers for a better understanding of insurance customers’ buying decision process.