Group decision making based on incomplete intuitionistic multiplicative preference relations

Jiang, Yuan; Zhang, Xu; Yu, Xiaohan

doi:10.1016/j.ins.2014.09.043

Cited by 63 publications

(98 citation statements)

References 35 publications

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“…For this proposed methodology, we develop the related methods to make them more effective. Example 1 can show the importance of considering geometry consistency degree, rather than paying more attention to calculating the missing elements in the reference of [44]. The illustrative study demonstrates the applicable of the proposed model.…”

Section: Conclusion and Further Researchmentioning

confidence: 91%

“…The final results calculated by Jiang et al [44] is: S(α 1 ) = 6.0127, S(α 2 ) = 0.5342, S(α 3 ) = 0.2766, S(α 4 ) = 0.4939, S(α 5 ) = 6.0250, with a ranking…”

Section: Definitionmentioning

confidence: 98%

“…Jiang et al [44] proposed the method that decomposed an intuitionistic multiplicative preference number U = (α ij ) n×n = (ρ ij , σ ij ) into two matrices: a preferred matrix C = (c ij ) n×n describing the positive information and a non-preferred matrix D = (d ij ) n×n describing the negative information given by DM with respect to alternative x i over x j [44]. Then, we develop the next definition to calculate the missing elements of an incomplete IMP's corresponding interval fuzzy preference relation matrix P = (p ij ) n×n by calculating the missing elements of P − = (c ij ) n×n and P + = (d ij ) n×n , respectively.…”

Section: Definitionmentioning

confidence: 99%

“…Further, decision makers and criteria can obtain their weights by the consistency degree. We use an example from Jiang et al [44], in which they focused on calculating the missing elements and did not give enough consideration to the consistency of decision matrices, to demonstrate the validity of our method.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation and Classification of Overseas Talents in China Based on the BWM for Intuitionistic Relations

et al. 2016

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Efficient utilization of human resources is an important force for the sustainable development of society and the economy. Against the backdrop of the development of economic globalization, the Chinese Government is presently implementing the strategy of "Strengthening the Nation with Talent" to assist the exploitation and management of human resources. Overseas talents have recently become an important resource. How to scientifically evaluate and classify overseas talents has become an important research topic, and it is necessary to seek a systematic decision aid. This paper introduces a novel methodology to evaluate and classify overseas talents in China under the intuitionistic relations environment. Firstly, we determine the weighted values of decision makers and criteria through defining geometry consistency. Secondly, we construct a non-linear Best-Worst-Method (BWM) model with intuitionistic preference relations. A highlight of this BWM model for intuitionistic relations is taking both positive and negative aspects into consideration, which is different from the original BWM. Finally, the proposed methodology is applied to an illustrative example of overseas talent evaluation, indicating the simultaneous efficiency and practicability of the method.

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Section: Conclusion and Further Researchmentioning

confidence: 91%

“…The final results calculated by Jiang et al [44] is: S(α 1 ) = 6.0127, S(α 2 ) = 0.5342, S(α 3 ) = 0.2766, S(α 4 ) = 0.4939, S(α 5 ) = 6.0250, with a ranking…”

Section: Definitionmentioning

confidence: 98%

Section: Definitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation and Classification of Overseas Talents in China Based on the BWM for Intuitionistic Relations

et al. 2016

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“…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

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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.

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Towards the significance of taxi recommender systems in smart cities

Katarya

2022

Concurrency and Computation

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Since their launch in the early 1990's, recommender systems (RSs) have played an essential role in information filtering and providing personalized information to users by utilizing the web, mobile, context, personalized, and practical applications. Many research articles have been published in various application domains such as movies, books, documents, news, images, music, shopping, TV programs, tourism, group, social, taxi, and others. However, from these domains, taxi recommendation is a new and thrust area of research, as publications in this domain are few and recently published.Taxi recommendations are associated with drivers-passengers with their: behavior, hotspots, trajectory, point of interest, route planning or prediction, taxi finding, anomaly or fraud detection, urban computing, mobility patterns and so forth. Due to this diversity and various agendas in taxi recommendations, more exploration is required, as it is a less mature and growing field than other domains. So we have done a literature review in the taxi domain by classification, incorporating reputed articles published from 2001 to August 2022. We include the reputed papers which highlight, analyze and perform studies only on the taxi domain. This article categorizes and discusses the research and development in taxi recommendations as nature, approaches, algorithms, methods, datasets, models, devices, information, and evaluation techniques. This survey article will actively help the researchers and professionals understand the current situations in taxi RS and resolve future trends, opportunities, and research.

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Group decision making based on incomplete intuitionistic multiplicative preference relations

Cited by 63 publications

References 35 publications

Evaluation and Classification of Overseas Talents in China Based on the BWM for Intuitionistic Relations

Evaluation and Classification of Overseas Talents in China Based on the BWM for Intuitionistic Relations

Uncertain Quality Function Deployment Using a Hybrid Group Decision Making Model

Towards the significance of taxi recommender systems in smart cities

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