Some Muirhead mean operators for probabilistic linguistic term sets and their applications to multiple attribute decision-making

Лю, Пэйдэ; Teng, Fei

doi:10.1016/j.asoc.2018.03.027

Cited by 123 publications

(66 citation statements)

References 28 publications

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“…So, the complexity of the proposed decision framework is O 3m 2 + nm 2 ≈ O m 2 . In contrary, the complexity of [27] (by similar analysis) is O(m 3 + m 2 + nm 2 ) ≈ O m 3 which is evidently complex than the proposed decision framework.…”

Section: Comparative Analysis: Plpr Based Decision Framework Vs Othersmentioning

confidence: 88%

“…(1) Unlike methods [21,27], the proposed framework can handle missing values in the PLPR in a much sensible and rational manner by automatically filling the missing values using a systematic procedure. (2) Though, method [27] presents a procedure for consistency check and repair, it is complex and computationally intensive as it involves Eigen vector calculation and uses logarithmic function.…”

Section: Comparative Analysis: Plpr Based Decision Framework Vs Othersmentioning

confidence: 99%

“…(3) Method [21] extends AHP under HFLTS context for ranking objects which loses potential probability information and hence, produces unreasonable ranking of objects. Further, method [27] extends AHP under PLTS context but, loses some information when transforming PLTS information to single values using possibility degree. To circumvent the issue, the proposed framework presents a method for ranking objects by extending the popular AHP under PLPR context.…”

Section: Comparative Analysis: Plpr Based Decision Framework Vs Othersmentioning

confidence: 99%

“…(5) Also, from the time complexity analysis, we can observe that proposed decision framework and method [27] has three crucial operations viz., (a) filling missing values, (b) check & repair of inconsistent and (c) ranking of objects with m objects and n attributes. Operation (a) takes O m 2 time complexity, operation (b) takes O m 2 time complexity and operation (c) takes O m 2 (n + 1) .…”

Section: Comparative Analysis: Plpr Based Decision Framework Vs Othersmentioning

confidence: 99%

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Probabilistic Linguistic Preference Relation-Based Decision Framework for Multi-Attribute Group Decision Making

et al. 2018

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With trending competition in decision-making process, linguistic decision-making is gaining attractive attention. Previous studies on linguistic decision-making have neglected the occurring probability (relative importance) of each linguistic term which causes unreasonable ranking of objects. Further, decision-makers' (DMs) often face difficulties in providing apt preference information for evaluation. Motivated by these challenges, in this paper, we set our proposal on probabilistic linguistic preference relation (PLPR)-based decision framework. The framework consists of two phases viz., (a) missing value entry phase and (b) ranking phase. In phase (a), the missing values of PLPR are filled using a newly proposed automatic procedure and consistency of PLPR is ensured using a consistency check and repair mechanism. Following this, in phase (b), objects are ranked using newly proposed analytic hierarchy process (AHP) method under PLPR context. The practicality of the proposal is validated by using two numerical examples viz., green supplier selection problem for healthcare and the automobile industry. Finally, the strength and weakness of the proposal are discussed by comparing with similar methods. Symmetry 2019, 11, 2 2 of 18 weighted geometric aggregation operator for a linguistic domain. Further, He et al. [8] put forward a new entropy measure for linguistic-based group decision-making.Though linguistic decision-making is an attractive concept, DMs still face difficulties in rationally rating the objects. The main reason for this difficulty is the cognitive behavior of the human mind, which encourages pair-wise comparative analysis rather than a standalone rating [9]. Motivated by the power of pair-wise comparison and linguistic term set (LTS), Herrera et al. [10,11] proposed the linguistic preference relation (LPR) concept for group decision-making and investigated some choice functions for the same. Following this, Xu [12] put forward some deviation measures for decision-making process under LPR context. Recently, Molinera et al. [13] developed new fuzzy ontologies for linguistic preference information and applied the same for decision-making. Wang and Xu [14] put forward an interactive algorithm for filling the missing LPR values using consistency measures and repaired the consistency of the same using the repairing mechanism.Inspired by the power of linguistic information and its substantial use in decision-making, Rodriguez [15] proposed the hesitant fuzzy linguistic term set (HFLTS) which is an extension to LTS under hesitant fuzzy environment [16]. The HFLTS allowed DMs to give different choices of preference for the same instance, which managed uncertainty to some extent. Motivated by the power of HFLTS, Zhu and Xu [17] put forward the hesitant fuzzy linguistic preference relation (HFLPR), which is an extension to preference relation under HFLTS context. They also investigated some consistency measures for the same. Following this, Wang and Xu [18] presented the concept of extended hesitant fuzzy prefe...

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Section: Comparative Analysis: Plpr Based Decision Framework Vs Othersmentioning

confidence: 88%

Section: Comparative Analysis: Plpr Based Decision Framework Vs Othersmentioning

confidence: 99%

Section: Comparative Analysis: Plpr Based Decision Framework Vs Othersmentioning

confidence: 99%

Section: Comparative Analysis: Plpr Based Decision Framework Vs Othersmentioning

confidence: 99%

See 2 more Smart Citations

Probabilistic Linguistic Preference Relation-Based Decision Framework for Multi-Attribute Group Decision Making

et al. 2018

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“…Lin, Chen, Liao and Xu [26] developed the ELECTRE II method to deal with PLTSs for edge computing. Liu and Teng [30] proposed some Muirhead mean operators for PLTSs. Liu and Teng [31] defined the probabilistic linguistic TODIM method.…”

Section: Introductionmentioning

confidence: 99%

GRA Method for Probabilistic Linguistic Multiple Attribute Group Decision Making with Incomplete Weight Information and Its Application to Waste Incineration Plants Location Problem

Lei¹,

Wei²,

Lu³

et al. 2019

IJCIS

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Multiple attribute group decision making (MAGDM) Probabilistic linguistic term sets (PLTSs) GRA method Incomplete weight information Waste incineration plants A B S T R A C TIn this essay, we investigate the probabilistic linguistic multiple attribute group decision making (PL-MAGDM) with incomplete weight information. In this method, the linguistic representation developed recently is converted into probabilistic linguistic information. For deriving the weight information of the attribute, an optimization model is built on the basis of the fundamental idea of grey relational analysis (GRA), by which the attribute weights can be decided. Then, the optimal alternative is chosen through calculating largest relative relational degree from the probabilistic linguistic positive ideal solution (PLPIS) which considers both the largest grey relational coefficient (GRC) from the PLPIS and the smallest GRC form probabilistic linguistic negative ideal solution (PLNIS). In the end, a case study concerning waste incineration plants location problem is given to demonstrate the merits of the developed methods.

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Someq‐rung orthopair fuzzy 2‐tuple linguistic Muirhead mean aggregation operators and their applications to multiple‐attribute group decision making

Wang

et al. 2019

Int J Intell Syst

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Multiple-attribute group decision making (MAGDM) under linguistic environment is an important part of modern decision sciences, and information aggregation operator plays an import role in solving MAGDM problems. In this paper, an approach for solving MAGDM problem with q-rung orthopair fuzzy 2-tuple linguistic information is developed. First, the q-rung orthopair fuzzy 2-tuple linguistic weighted averaging (q-ROFTLWA) operator and the q-rung orthopair fuzzy 2-tuple linguistic weighted geometric (q-ROFTLWG) operator are presented. Furthermore, the q-rung orthopair fuzzy 2-tuple linguistic Muirhead mean (q-ROFTLMM) operator and the q-rung orthopair fuzzy 2-tuple linguistic dual Muirhead mean (q-ROFTLDMM) operator are proposed on the basis of Muirhead mean (MM) operator and dual Muirhead mean (DMM) operator. Then, an approach is developed to deal with MAGDM problem under q-rung orthopair fuzzy 2-tuple linguistic environment based on the proposed operators. Finally, a numerical example for selecting desirable emergency alternative(s) in the process of designing emergency preplan is given to illustrate the application of the developed method and demonstrate its effectiveness.

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Some Muirhead mean operators for probabilistic linguistic term sets and their applications to multiple attribute decision-making

Cited by 123 publications

References 28 publications

Probabilistic Linguistic Preference Relation-Based Decision Framework for Multi-Attribute Group Decision Making

Probabilistic Linguistic Preference Relation-Based Decision Framework for Multi-Attribute Group Decision Making

GRA Method for Probabilistic Linguistic Multiple Attribute Group Decision Making with Incomplete Weight Information and Its Application to Waste Incineration Plants Location Problem

Someq‐rung orthopair fuzzy 2‐tuple linguistic Muirhead mean aggregation operators and their applications to multiple‐attribute group decision making

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