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 pre...
详细信息
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.
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 mul...
详细信息
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.
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 ...
详细信息
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.
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 nec...
详细信息
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.
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 tr...
详细信息
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.
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...
详细信息
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.
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 involv...
详细信息
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.
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...
详细信息
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.
暂无评论