The consensus of probabilistic uncertain linguistic preference relations and the application on the virtual reality industry

Xie, Wanying; Ren, Zhiliang; Zhang, Xu; Wang, Hai

doi:10.1016/j.knosys.2018.07.016

Cited by 55 publications

(14 citation statements)

References 43 publications

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“…Liao and Xu (2015) simulated a process for selecting ERP system for enterprises, in which several indicators for ERP system were proposed and the process turns out to be operable. Xie et al (2018) analyzed the indicators for a company to cooperate with others and simulated to help a virtual reality company to choose one experienced company to cooperate with through applying the consensus reaching model. For the human resource evaluation, Wang (2015) applied the proposed decision-making methods to evaluate the university faculty for tenure and promotion.…”

Section: Applications Of Recent Decision-making Methods With Linguistmentioning

confidence: 99%

Group Decisions with Linguistic Information: A Survey

He¹,

Zhang²

2020

Handbook of Group Decision and Negotiation

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Section: Applications Of Recent Decision-making Methods With Linguistmentioning

confidence: 99%

Group Decisions with Linguistic Information: A Survey

He¹,

Zhang²

2020

Handbook of Group Decision and Negotiation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Specifically speaking, compared with the model proposed by the existing methods, the specific advantages of the model proposed in this paper are as follows (1) Compared with models M-1 and M-3 in method [27], the model proposed in this paper can directly obtain the priority weight of preference relation without consistency test and correction. (2) Compared with Algorithms 1 and 2 in method [37], the algorithm proposed in this paper provides a method to determine the individual weight, and the consensus collective preference relation can be obtained directly without iterative calculation. (3) Compared with the model proposed by wan et al [36], the model proposed in this paper considers the probability distribution of uncertain information, which is more suitable for large-scale GDM problems in complex environments and can ensure the consistency of collective preference relations.…”

Section: Comparison Analysesmentioning

confidence: 99%

A Programming-Based Algorithm for Probabilistic Uncertain Linguistic Intuitionistic Fuzzy Group Decision-Making

Gong

Chen

2019

Symmetry

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As an effective tool to express the subjective preferences of decision makers, the linguistic term sets (LTS) have been widely used in group decision-making (GDM) problems, such as hesitant fuzzy LTS, linguistic hesitant fuzzy sets, probabilistic LTS, etc. However, due to the increasing complexity of practical decision-making (DM) problems, LTS still has a lot of room to expand in fuzzy theory. Qualitative uncertainty information in the application of GDM is yet to be improved. Therefore, in order to improve the applicability of linguistic terms in DM problems, a probabilistic uncertain linguistic intuitionistic fuzzy set (PULIFS) that can fully express the decision-maker’s (DM’s) evaluation information is first proposed. To improve the rationality of DM results, we give a method for determining individual weights in the probabilistic uncertain linguistic intuitionistic fuzzy preference relation (PULIFPR) environment. In addition, we present two consistency definitions of PULIFPR to reflect both the assessment information and risk attitudes of decision makers. Subsequently, a series of goal programming models (GPMs) are established, which effectively avoid the consistency check and correction process of existing methods. Finally, the developed method is applied to an empirical example concerning the selection of a virtual reality (VR) project. The advantages of the proposed method are demonstrated by comparative analysis.

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“…Pang et al [11] thus introduced the probabilistic linguistic term set (PLTS), which is capable of dealing with situations where probabilistic distributions cannot be acquired completely in reality, and is capable of carrying out probability information aggregation without a loss of information [24]. Since its introduction, PLTS has been successfully applied to solve practical MADM problems in various settings [38][39][40][41][42][43][44][45][46]. As can be seen, these studies all only allow decision makers to depict their cognitive models through balanced linguistic terms sets (BLTS) [25] which are uniformly and symmetrically distributed.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Hybrid Linguistic Approaches for Multiple Attribute Group Decision Making with Decision Hesitancy and the Prioritization of Attribute Relationships

Zhang

Hong

et al. 2020

Symmetry

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Focusing on ill-structured multiple attribute decision-making (MADM) problems, including decision hesitancy and attribute prioritization relationships, this paper investigates appropriate approaches for decision making. Firstly, we introduce the probabilistic hybrid linguistic term set (P-HLTS) for capturing probabilistic preferences about possible linguistic labels belonging to a wide range of hesitant linguistic term sets. Entropy and distance measurements for P-HLTS are developed without arbitrary complementing operations. To facilitate decision making with attribute prioritization relationships, we present a probabilistic uncertain balanced linguistic-prioritized weighted average (PUBL-PWA) operator and the probabilistic uncertain balanced linguistic-induced prioritized ordered weighted average (PUBL-IPOWA) operator. In terms of the strength of the above tools, we further construct two multiple attribute group decision-making (MAGDM) approaches under P-HLTS environments, namely, an approach for decision-making situations where attribute prioritization relationships are known in advance and the relative importance of decision makers (DMs) or decision-making units (DMUs) is not required for consideration, and second approach for decision-making situations where both attribute prioritization relationships and the weighted vectors of DMs or DMUs are explicitly unknown. In general, our proposed approaches are more flexible and practical when considering heterogeneous opinions, avoiding information distortion brought about by complementing operation-based distance measures. Furthermore, illustrative application studies are conducted to verify our developed approaches.

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The consensus of probabilistic uncertain linguistic preference relations and the application on the virtual reality industry

Cited by 55 publications

References 43 publications

Group Decisions with Linguistic Information: A Survey

Group Decisions with Linguistic Information: A Survey

A Programming-Based Algorithm for Probabilistic Uncertain Linguistic Intuitionistic Fuzzy Group Decision-Making

Probabilistic Hybrid Linguistic Approaches for Multiple Attribute Group Decision Making with Decision Hesitancy and the Prioritization of Attribute Relationships

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