Fostering linguistic decision-making under uncertainty: A proportional interval type-2 hesitant fuzzy TOPSIS approach based on Hamacher aggregation operators and andness optimization models

Chen, Zhen‐Song; Yang, Yi; Wang, Xianjia; Chin, Kwai‐Sang; Tsui, Kwok‐Leung

doi:10.1016/j.ins.2019.05.074

Cited by 136 publications

(63 citation statements)

References 38 publications

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“…According to the results of the early warning model, the Chinese government should promote the real estate market, improve the real estate system, broaden real estate financing channels, and strengthen publicity for real estate market regulation policies, in order to prevent the formation of a bubble in the Chinese real estate market and to achieve a balance between supply and demand and healthy development. In our future work, we will investigate the evaluation frameworks of existing early warning models based on the ELECTRE III, TOPSIS with the integration of proportional hesitant fuzzy linguistic information [75,76].…”

Section: Resultsmentioning

confidence: 99%

Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing

Wang¹,

Zeng²,

Zhang³

et al. 2020

IJCIS

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Based on a multi-class support vector machine, an urban real estate early warning model is constructed for the Beijing real estate market. The initial indicator system is established based on the historical development of Beijing's real estate market and the selection of real estate early warning indicators. Early warning index data for Beijing from 2000 to 2018 are selected, and the leading index is selected by a time difference correlation analysis as the warning index to be used for further implementation. The model is found to have a good early warning judgment performance, and demonstrates generalization ability. The model analyzes the real estate market from the aspects of land supply, credit scale, housing supply structure, restriction of speculation, and strengthening of transparency of real estate information. It predicts that the real estate market in Beijing will run smoothly in 2019. Based on the model's findings, the paper proposes policy recommendations to promote the healthy operation of China's real estate market.

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Section: Resultsmentioning

confidence: 99%

Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing

Wang¹,

Zeng²,

Zhang³

et al. 2020

IJCIS

Self Cite

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“…In future research, with the increasingly complicated of GDM issues in practice, the incomplete evaluation information will inevitably be expressed; thus, we will improve the proposed GDM approach to deal with the GDM problems with incomplete HMPRs. Furthermore, the large-scale GDM problems have received more and more attention in the existing literature [55][56][57][58][59]; then, the GDM approach based on HMPRs, in which a large-scale group of decision makers participates in the evaluation process, is also a future research focus. Further endeavors will also be devoted to the incorporation of the attitudinal dimension into HMPRs [18,60,61].…”

Section: Discussionmentioning

confidence: 99%

Revisiting the Role of Hesitant Multiplicative Preference Relations in Group Decision Making With Novel Consistency Improving and Consensus Reaching Processes

Wang¹,

Shuai²,

Chen³

et al. 2019

IJCIS

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In recent years, hesitant multiplicative preference relation (HMPR) has been a powerful means to represent the evaluation information of decision makers during the pairwise comparison concerning alternatives. As the important parts of group decision making (GDM) issues with HMPRs, the consistency improving and consensus reaching processes have been researched by many scholars; however, the existing approaches present several limitations, including defining the consistency index depend on the other HMPR instead of the original HMPR, improving the consistency and consensus levels of HMPRs independently, and that the high computational complexity of the existing iterative algorithms. To overcome these drawbacks, this paper proposes a mathematical programming model to improve the consistency and consensus levels of HMPRs, simultaneously. First, a consistency index according to multiplicative consistency is put forward to compute the consistency degree of normalized HMPR (NHMPR) after the normalization procedure. Second, a programming model by minimizing the difference between the original NHMPR and the revised NHMPR is developed to obtain the acceptably consistent NHMPR; then, a consistency-based method is constructed to solve the decision making problems with an HMPR. Third, considering the GDM issues, a consistency-and consensus-based programming model is established to obtain the acceptably consistent and consensus NHMPRs, in which the original evaluation information can remain as much as possible. Fourth, the normalized hesitant multiplicative weighted geometric operator is introduced to fuse the revised NHMPRs and an algorithm for GDM is proposed with novel consistency improving and consensus reaching processes. Finally, two numerical examples are applied to show the practicality and advantages of the proposed approaches.

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“…The fuzzy set theory, which was introduced by Zhdeh [1], has created massive progress in the representation of uncertain and ambiguous data and has successfully been applied in the different area. Following Zadeh [1], some authors developed the fuzzy set theory and presented its extended forms such as the interval-valued fuzzy sets [2], the type-2 fuzzy sets [3], the fuzzy multi-sets [4], the intuitionistic fuzzy sets [5], the hesitant fuzzy sets [6], the interval-valued hesitant fuzzy sets [7], picture fuzzy sets [8], Picture Hesitant Fuzzy Set [9], Proportional hesitant fuzzy linguistic term set [10] and proportional interval type-2 hesitant fuzzy set [11]. However, the fuzzy set theory and its extended forms are inefficient at representing the reliability of the information.…”

Section: Introductionmentioning

confidence: 99%

Linguistic Z-number weighted averaging operators and their application to portfolio selection problem

et al. 2020

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Z-numbers can generate a more flexible structure to model the real information because of capturing expert's reliability. Moreover, various semantics can flexibly be reflected by linguistic terms under various circumstances. Thus, this study aims to model the portfolio selection problems based on aggregation operators under linguistic Z-number environment. Therefore, a multi-stage methodology is proposed and linguistic Z-numbers are applied to describe the assessment information. Moreover, the weighted averaging (WA) aggregation operator, the ordered weighted averaging (OWA) aggregation operator and the hybrid weighted averaging (HWA) aggregation operator are developed to fuse the input arguments under the linguistic Z-number environment. Then, using the max-score rule and the scoreaccuracy trade-off rule, three qualitative portfolio models are presented to allocate the optimal assets. These models are suitable for general investors and risky investors. Finally, to illustrate the validity of the proposed qualitative approach, a real case including 20 corporations of Tehran stock exchange market in Iran is provided and the obtained results are analyzed. The results show that combining linguistic Z-numbers with portfolio selection processes can increase the tendencies and capabilities of investors in the capital market and it helps them manage their portfolios efficiently.

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Fostering linguistic decision-making under uncertainty: A proportional interval type-2 hesitant fuzzy TOPSIS approach based on Hamacher aggregation operators and andness optimization models

Cited by 136 publications

References 38 publications

Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing

Urban Real Estate Market Early Warning Based on Support Vector Machine: A Case Study of Beijing

Revisiting the Role of Hesitant Multiplicative Preference Relations in Group Decision Making With Novel Consistency Improving and Consensus Reaching Processes

Linguistic Z-number weighted averaging operators and their application to portfolio selection problem

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