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Incomplete Hesitant Fuzzy preference Relations in Group Decision Making

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2017年第3期19卷 637-645页

作者： Khalid, Asma Beg, Ismat Lahore Sch Econ Lahore Punjab Pakistan

In this article, incomplete hesitant fuzzy preference relations are under consideration. In order to estimate expressible missing preferences, a hesitant upper bound condition (hubc) is defined for decision makers presenting incomplete information. With the help of this condition, the estimated preference intensities lie inside the defined domain and thus are expressible. An algorithm is proposed to revise minimal possible preferences so that the resultant satisfies property (hubc). Moreover, ranking rule, HF-Borda count, for hesitant fuzzy preference relations is defined. This method dissolves possible ties among alternatives.

关键词： Hesitant fuzzy preference relation Incomplete preference relation Decision making preference modeling

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Team Formation Through preference-Based Behavior Composition 1

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15th German Conference on Multiagent System Technologies (MATES)

作者： Barati, Masoud St-Denis, Richard Univ Sherbrooke Dept Informat Sherbrooke PQ J1K 2R1 Canada

ISBN: (数字)9783319647982

ISBN: (纸本)9783319647982;9783319647975

A team formation problem consists in finding an effective group of experts in a social network to accomplish a job with a minimum expenditure of energy and time. This problem has been transposed into the domain of multiagent systems to form a team of autonomous agents whose mission is to achieve a given goal. There is a wide range of such problems. This paper generalizes one of them by assigning explicit behaviors to agents whose tasks are equipped with multiple attributes. Their values are compared with preferences attached to the desired tasks of the goal. A synthesized controller realizes the goal by invoking tasks of a subset of the available agents, called a composition in this paper. Furthermore, utility values are assigned to compositions and robustness is considered to be an important property of a team to prevent its deterioration when one or more of its agents fail. Finding a robust team that satisfies the goal's preferences with better utility values for compositions constitutes a difficult optimization problem. The proposed method to solve this problem consists in three phases: controller synthesis with filtering on tasks with respect to some qualitative preferences, composition ranking based on their fitness, and multiobjective mathematical optimization.

关键词： Multiagent system Team formation preference modeling Control Planning Multiobjective optimization

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modeling project preferences in multiattribute portfolio decision analysis

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第1期263卷 225-239页

作者： Tervonen, Tommi Liesio, Juuso Salo, Ahti Evidera London England Aalto Univ Sch Business Dept Informat & Serv Econ Espoo Finland Aalto Univ Sch Sci Dept Math & Syst Anal Syst Anal Lab Espoo Finland

When choosing a portfolio of projects with a multi-attribute weighting model, it is necessary to elicit trade-off statements about how important these attributes are relative to each other. Such statements correspond to weight constraints, and thus impact on which project portfolios are potentially optimal or non-dominated in view of the resulting set of feasible attribute weights. In this paper, we extend earlier preference elicitation approaches by allowing the decision maker to make direct statements about the selection and rejection of individual projects. We convert such project preference statements to weight information by determining the weights for which (i) the selected project is included in all potentially optimal or non-dominated portfolios, or (ii) the rejected project is not included in any potentially optimal or non-dominated portfolio. We prove that the two complementary selection rules will exclude exactly the same set of weights. However, analyses that apply the dominance structure often lead to multiple, mutually exclusive feasible weight sets, and therefore the approach based on potential optimality is more relevant for practical decision analysis. We also propose ex ante value of information measures to guide the elicitation of project preference statements, and illustrate our results by analyzing a real case on the selection of infrastructure maintenance projects. (C) 2017 The Authors. Published by Elsevier B.V.

关键词： Multiple criteria analysis Project portfolio selection Multi-attribute value theory preference modeling

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Self-dual operators and a general framework for weighted nilpotent operators

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2017年 81卷 115-127页

作者： Dombi, J. Csiszar, O. Univ Szeged Inst Informat Szeged Hungary Obuda Univ Inst Appl Math Budapest Hungary

The main purpose of this paper is to consider generated nilpotent operators in an integrative frame and to examine the nilpotent aggregative operator. As a starting point, instead of associativity, we focus on the necessary and sufficient condition of the self dual property. A parametric form of the generated operator o(nu) is given by using a shifting transformation of the generator function. The parameter has an important semantical meaning as a threshold of expectancy (decision level). Nilpotent conjunctive, disjunctive, aggregative and negation operators can be obtained by changing the parameter value. The properties (De Morgan property, commutativity, self-duality, fulfillment of the boundary conditions, bisymmetry) of the weighted general operator are examined and the formula of the commutative self-dual generated operator, the so-called weighted aggregative operator is given. It is proved that the two-variable operator with weights w(1) = w(2) = 1 (SIC)i is conjunctive for low input values, disjunctive for high ones, and averaging otherwise;i.e. a high input can compensate for a lower one. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Nilpotent operators Bounded system Aggregative operator Weighted aggregation Uninorm preference modeling

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Necessary and possible indifferences

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JOURNAL OF MATHEMATICAL PSYCHOLOGY 2017年 81卷 98-109页

作者： Giarlotta, Alfio Watson, Stephen Univ Catania Dept Econ & Business Catania Italy York Univ Dept Math & Stat Toronto ON Canada

A NaP-preference (necessary and possible preference) is a pair of nested reflexive relations on a set such that the smaller is transitive, the larger is complete, and the two relations jointly satisfy properties of transitive coherence and mixed completeness. It is known that a NaP-preference is characterized by the existence of a set of total preorders whose intersection and union give its two components. We introduce the symmetric counterpart of a NaP-preference, called a NaP-indifference: this is a pair of nested symmetric relations on a set such the smaller is an equivalence relation, and the larger is a transitively coherent extension of the first. A NaP-indifference can be characterized by the existence of a set of equivalence relations whose intersection and union give its two components. NaP-indifferences naturally arise in applications: for instance, in the field of individual choice theory, suitable pairs of similarity relations revealed by a choice correspondence yield a NaP-indifference. We classify NaP-indifferences in two categories, according to their genesis: (i) derived, which are canonically obtained by taking the symmetric part of a NaP-preference;(ii) primitive, which arise independently of the existence of an underlying NaP-preference. This partition into two classes turns out to be related to the notion of incomparability graph. (C) 2017 Elsevier Inc. All rights reserved.

关键词： preference modeling NaP-preference NaP-indifference Resolution Equivalence relation Similarity relation Revealed preference Primitive similarity Comparability graph

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Fusion of preferences from different perspectives in a decision-making context

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INFORMATION FUSION 2016年 29卷 120-131页

作者： Tapia-Rosero, A. Bronselaer, A. De Mol, R. De Tre, G. Escuela Super Politecn Litoral Dept Elect & Comp Engn Guayaquil Ecuador Univ Ghent Dept Telecommun & Informat Proc B-9000 Ghent Belgium

Solving a decision-making problem about a brand-new product might include preferences from a high number of potential customers (e.g., followers of a company on social media) and managerial constraints (or preferences) given by corporate managers with regard to different aspects (i.e., economical, technical, environmental, etc.) over multiple criteria (e.g., weight, capacity, color, or usefulness of a product). These give us some new insights on fusing preferences given by persons having different perspectives (e.g., economical, technical, environmental, etc.), including decision-makers, and aimed to be suitable for different organizational structures (e.g., multilevel structures). Herein, a proper representation is needed to merge preferences from each perspective, enabling their propagation, throughout an organizational structure until the level in which a decision is made. This representation is presented as a decision-making unit (DMU), and is used as the primary component of our decision-making model. In this paper, we propose a novel decision-making model that recursively merges the preferred criteria from different DMUs using the logic scoring of preference (ISP) method. An illustrative example demonstrating the applicability of the proposed model, in the context of a new product design, is included in the paper. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Decision-making unit Different perspectives Multilevel fusion preference modeling Recursive LSP

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A Kansei evaluation approach based on the technique of computing with words

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ADVANCED ENGINEERING INFORMATICS 2016年第1期30卷 1-15页

作者： Chou, Jyh-Rong I Shou Univ Dept Creat Prod Design 1Sec 1Syuecheng Rd Kaohsiung 84001 Taiwan

Kansei evaluation plays a vital role in the implementation of Kansei engineering;however, it is difficult to quantitatively evaluate customer preferences of a product's Kansei attributes as such preferences involve human perceptual interpretation with certain subjectivity, uncertainty, and imprecision. An effective Kansei evaluation requires justifying the classification of Kansei attributes extracted from a set of collected Kansei words, establishing priorities for customer preferences of product alternatives with respect to each attribute, and synthesizing the priorities for the evaluated alternatives. Moreover, psychometric Kansei evaluation systems essentially require dealing with Kansei words. This paper presents a Kansei evaluation approach based on the technique of computing with words (CWW). The aims of this study were (1) to classify collected Kansei words into a set of Kansei attributes by using cluster analysis based on fuzzy relations;(2) to model Kansei preferences based on semantic labels for the priority analysis;and (3) to synthesize priority information and rank the order of decision alternatives by means of the linguistic aggregation operation. An empirical study is presented to demonstrate the implementation process and applicability of the proposed Kansei evaluation approach. The theoretical and practical implications of the proposed approach are also discussed. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Kansei evaluation preference modeling Kansei clustering Linguistic aggregation Computing with words

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Interactive and nonparametric modeling of preferences on an ordinal scale using small data

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 65卷 345-360页

作者： Eriskin, Levent Koksal, Gulser Middle East Tech Univ Dept Ind Engn TR-06800 Ankara Turkey

In this study, we consider learning preference structure of a Decision Maker (DM). Many preference modeling problems in a variety of fields such as marketing, quality control and economics involve possibly interacting criteria, and an ordinal scale is used to express preference of objects. In these cases, typically underlying preference structure of the DM and distribution of criteria values are not known, and only a few data can be collected about the preferences of the DM. For developing a preference model under such circumstances, we propose using nonparametric Statistical Learning approaches interactively. In particular, we employ Active Learning by asking a preference question to the DM at each step and try to reach a close approximation to the correct model in a small number of steps. Our experimental analysis proves that the proposed approach outperforms a "naive" approach where subsequent questions are asked randomly. In the study, we also provide algorithmic recommendations for modeling different underlying value functions, if information is available about the form of the preference structure and/or distribution of criteria values. This study can be regarded as a pioneering approach considering that Statistical Learning based approaches in the literature have been developed and tested based on a relatively large preference information and they do not interact with the DM in model developing process while Multi Criteria Decision Aid based approaches typically ignore interactions among the criteria, suffer from generalization ability, and have no concern about predicting equally good everywhere in the criteria domain. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： preference modeling Sorting Active learning Interactive approach Multi criteria decision aid

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A NETWORK APPROACH FOR UNDERSTANDING AND ANALYZING PRODUCT CO-CONSIDERATION RELATIONS IN ENGINEERING DESIGN 14

A NETWORK APPROACH FOR UNDERSTANDING AND ANALYZING PRODUCT C...

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14th International Design Conference (DESIGN)

作者： Wang, M. Huang, Y. Contractor, N. Fu, Y. Chen, W. Northwestern Univ Dept Mech Engn 2145 Sheridan RdTech B224 Evanston IL 60208 USA

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Mit :: Lcs :: Tr :: Mit-Lcs-Tr-340

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2016年

Mit :: Lcs :: Tr :: Mit-Lcs-Tr-340 by published by

关键词： attributes decision making functional form functional forms independence knowledge knowledge base mit-lcs-tr-340 modeling multiattribute multiattribute utility preference preference knowledge preference modeling qualitative qualitative behaviors reasoning urp utility utility function utility functions

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