Managing incomplete preference relations in decision making: A review and future trends

Ureña, Raquel; Chiclana, Francisco; Morente-Molinera, Juan Antonio; Herrera‐Viedma, Enrique

doi:10.1016/j.ins.2014.12.061

Cited by 248 publications

(91 citation statements)

References 100 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Most of the methods dealing with incomplete fuzzy relations in the literature are articulated in several steps, one of them should be a completion phase, where the unknown elements are replaced by numerical values following different strategies [2], [14], in [29] a complete review is included.…”

Section: Rank Aggregation Methods Dealing With Partial Listsmentioning

confidence: 99%

Rank aggregation methods dealing with incomplete information applied to Smart Cities

Dopazo

Martínez-Cespedes

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

Abstract-City-rankings have become a central instrument for assessing the attractiveness of urban regions over the last years. Demographic, environmental, economic, political and socio-cultural factors are forcing the urban world to design and implement Smart Cities. A set of multidimensional components underlies the fuzzy smart city concept. As a result cities are evaluated and ranked with regard to different characteristics, and smart city measures are achieved through chosen indicators. Therefore, the problem of combining multiple rankings to form an aggregate ranking, which compares city performance, is recognized as a useful tool in this context. Moreover, a usual situation is when incomplete information arises and only partial rankings may be supplied. This paper addresses the general problem of rank aggregation dealing with incomplete information based on rank aggregation methods and multicriteria decision making theory. It consists on constructing a consensus ranking from partial rankings of a set of objects provided according different criteria. Our techniques rely on outranking matrices as a way of collecting relevance information from input data, theory of fuzzy preference relations and the PageRank algorithm.

show abstract

Section: Rank Aggregation Methods Dealing With Partial Listsmentioning

confidence: 99%

Rank aggregation methods dealing with incomplete information applied to Smart Cities

Dopazo

Martínez-Cespedes

2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

View full text Add to dashboard Cite

show abstract

“…The management of incomplete information has been studied by many researchers [45,46], and lots of methods have been developed for the determination of criteria weights with incomplete information, such as those based on technique for order preference by similarity to an ideal solution (TOPSIS) [19], distance measure [47] and entropy method [48]. In the QFD literature, however, little research has been conducted to estimate the weights of CRs when the weight information is incompletely known.…”

Section: Determine the Importance Weights Of Crsmentioning

confidence: 99%

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

2016

View full text Add to dashboard Cite

Quality function deployment (QFD) is a widely used quality system tool for translating customer requirements (CRs) into the engineering design requirements (DRs) of products or services. The conventional QFD analysis, however, has been criticized as having some limitations such as in the assessment of relationships between CRs and DRs, the determination of CR weights and the prioritization of DRs. This paper aims to develop a new hybrid group decision-making model based on hesitant 2-tuple linguistic term sets and an extended QUALIFLEX (qualitative flexible multiple criteria method) approach for handling QFD problems with incomplete weight information. First, hesitant linguistic term sets are combined with interval 2-tuple linguistic variables to express various uncertainties in the assessment information of QFD team members. Borrowing the idea of grey relational analysis (GRA), a multiple objective optimization model is constructed to determine the relative weights of CRs. Then, an extended QUALIFLEX approach with an inclusion comparison method is suggested to determine the ranking of the DRs identified in QFD. Finally, an analysis of a market segment selection problem is conducted to demonstrate and validate the proposed QFD approach.

show abstract

“…In these situations, decision makers may provide their preference information in the form of incomplete preference relations, i.e. a preference relation with some of its elements missing [1,23,24,[38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Group decision-making based on heterogeneous preference relations with self-confidence

Liu

Dong

Chiclana

et al. 2016

Fuzzy Optim Decis Making

172

View full text Add to dashboard Cite

Preference relations are very useful to express decision makers' preferences over alternatives in the process of group decision-making. However, the multiple self-confidence levels are not considered in existing preference relations. In this study, we define the preference relation with self-confidence by taking multiple self-confidence levels into consideration, and we call it the preference relation with self-confidence. Furthermore, we present a two-stage linear programming model for estimating the collective preference vector for the group decision-making based on heterogeneous preference relations with self-confidence. Finally, numerical examples are used to illustrate the two-stage linear programming model, and a comparative analysis is carried out to show how self-confidence levels influence on the group decision-making results.

show abstract

Managing incomplete preference relations in decision making: A review and future trends

Cited by 248 publications

References 100 publications

Rank aggregation methods dealing with incomplete information applied to Smart Cities

Rank aggregation methods dealing with incomplete information applied to Smart Cities

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

Group decision-making based on heterogeneous preference relations with self-confidence

Contact Info

Product

Resources

About