Rational decision making models with incomplete weight information for production line assessment

Pei, Zhi

doi:10.1016/j.ins.2012.07.060

Cited by 41 publications

(17 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Different approaches have been developed for dealing with interval number such as Zhou (2011), Liu et al (2012), Pei (2013), Xu (2013), Xu and Cai (2012), Yue (2013). In order to measure deviation between interval numbers, Xu (2008) introduced a distance for each pair of interval numbers as following:…”

Section: Interval Numbersmentioning

confidence: 99%

Uncertain Group Decision-Making With Induced Aggregation Operators and Euclidean Distance

Zeng

2013

Technological and Economic Development of Economy

View full text Add to dashboard Cite

In this paper, we present the induced uncertain Euclidean ordered weighted averaging distance (IUEOWAD) operator. It is an extension of the OWA operator that uses the main characteristics of the induced OWA (IOWA), the Euclidean distance and uncertain information represented by interval numbers. The main advantage of this operator is that it is able to consider complex attitudinal characters of the decision-maker by using order-inducing variables in the aggregation of the Euclidean distance. Moreover, it is able to deal with uncertain environments where the information is very imprecise and can be assessed with interval numbers. We study some of its main properties and particular cases such as the uncertain maximum distance, the uncertain minimum distance, the uncertain normalized Euclidean distance (UNED), the uncertain weighted Euclidean distance (UWED) and the uncertain Euclidean ordered weighted averaging distance (UEOWAD) operator. We also apply this aggregation operator to a group decision-making problem regarding the selection new artillery weapons under uncertainty.A wide range of aggregation operators are found in the literature (Beliakov et al. 2007;Calvo et al. 2002;Torra, Narukawa 2007;Yager et al. 2011). One common aggregation method is the ordered weighted averaging (OWA) operator (Yager 1988). It provides a parameterized family of aggregation operators that include as special cases the maximum, the minimum and the average. Since its appearance, the OWA operator has been used in a wide range of applications (Chang, Wen An interesting extension of the OWA operator is the induced OWA (IOWA) operator (Yager, Filev 1999). The difference is that the reordering step is no longer determined only by the values of the arguments, but could be induced by another mechanism, such as the ordered position of the arguments; in other words, the reordering can depend on the values of their associated order-inducing variables. In the last few years, the IOWA operator has received increasing attention, e.g. to use another approach that is able to assess the uncertainty such as the use of interval numbers (Moore 1966; Xu, Da 2002). By using interval numbers we can consider a wide range of possible results between the maximum and the minimum. In order to extend the IOWA operator to accommodate these uncertain situations, Xu (2006) developed the uncertain IOWA (UIOWA) operator. Basically, it is an aggregation operator that deals with uncertain information represented in the form of interval numbers. Since its introduction, several authors have developed further improvements. For example, Merigó and Casanovas (2011d) generalized it by using generalized and quasi-arithmetic means and developed the uncertain induced quasi-arithmetic OWA (Quasi-UIOWA) operator. Based on the heavy OWA (HOWA) operator, Merigó and Casanovas (2011e) developed the uncertain induced heavy OWA (UIHOWA) operator. Merigó et al. (2012) developed the uncertain induced ordered weighted averaging-weighted averaging (UIOWAWA) operator.A further interesting...

show abstract

Section: Interval Numbersmentioning

confidence: 99%

Uncertain Group Decision-Making With Induced Aggregation Operators and Euclidean Distance

Zeng

2013

Technological and Economic Development of Economy

View full text Add to dashboard Cite

show abstract

“…Over the past decades, MAGDM problems with incomplete weight information have received great attention (Chen & Yang, 2011;Ju, 2014;Ju & Wang, 2013;Li & Wan, 2013;Pei, 2013;Wei, 2008;Xu & Zhang, 2013;Zhang & Guo, 2012). Xu and Zhang (2013) proposed an optimal method for hesitant fuzzy preference relation with incomplete weight information.…”

Section: Introductionmentioning

confidence: 99%

A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

Zhang

Wang

2015

International Journal of Systems Science

View full text Add to dashboard Cite

The aim of this paper is to put forward a consensus reaching method for multi-attribute group decision-making (MAGDM) problems with linguistic information, in which the weight information of experts and attributes is unknown. First, some basic concepts and operational laws of 2-tuple linguistic label are introduced. Then, a grey relational analysis method and a maximising deviation method are proposed to calculate the incomplete weight information of experts and attributes respectively. To eliminate the conflict in the group, a weight-updating model is employed to derive the weights of experts based on their contribution to the consensus reaching process. After conflict elimination, the final group preference can be obtained which will give the ranking of the alternatives. The model can effectively avoid information distortion which is occurred regularly in the linguistic information processing. Finally, an illustrative example is given to illustrate the application of the proposed method and comparative analysis with the existing methods are offered to show the advantages of the proposed method.

show abstract

“…Objective methods use a decision matrix to determine attribute weights (e.g., Chen & Li, 2010Deng, Yeh, & Willis, 2000;Wang, 1998;Wang & Luo, 2010;Xu & Xia, 2012). Hybrid methods combine the preferences of a decision maker with a decision matrix to determine attribute weights (e.g., Fan, Ma, & Zhang, 2002;Ma, Fan, & Huang, 1999;Pei, 2013;Rao, Patel, & Parnichkun, 2011;Wang & Parkan, 2006).…”

Section: Introductionmentioning

confidence: 99%

An interval difference based evidential reasoning approach with unknown attribute weights and utilities of assessment grades

Wang

2015

Computers & Industrial Engineering

View full text Add to dashboard Cite

Rational decision making models with incomplete weight information for production line assessment

Cited by 41 publications

References 33 publications

Uncertain Group Decision-Making With Induced Aggregation Operators and Euclidean Distance

Uncertain Group Decision-Making With Induced Aggregation Operators and Euclidean Distance

A consensus reaching model for 2-tuple linguistic multiple attribute group decision making with incomplete weight information

An interval difference based evidential reasoning approach with unknown attribute weights and utilities of assessment grades

Contact Info

Product

Resources

About