The Extension of TOPSIS Method for Multi-Attribute Decision-Making With q-Rung Orthopair Hesitant Fuzzy Sets

Wang, Yuhui; Shan, Zhifeng; Huang, Lin

doi:10.1109/access.2020.3018542

Cited by 25 publications

(11 citation statements)

References 55 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, the H q ROFS is a T-SHFS with indeterminacy term equal zero, so it cannot handle the T-SHFNs. Thus, to compare the suggested method with these in [19], we put the degree of indeterminacy to 0 in the suggested operator. For PHFS and SHFS they are also considered as special cases of T-SHFS with q = 1 and q = 2, respectively.…”

Section: The Comparison Analysis With the Other Methodsmentioning

confidence: 99%

“…al [18] also presented a new concept of hesitant q-rung orthopair fuzzy set (H q ROFS) and proposed the H q ROF weighted averaging (H q ROFWA) and H q ROF weighted geometric (H q ROFWG)operators. Wang et al [19] defined the distance, similarity measures and the entropy of q-ROHFSs. Based on these concepts, they constructed a TOPSIS model under the q-ROHF environment.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A New Multi Attribute Decision Making Method Based on the T-Spherical Hesitant Fuzzy Sets

Al-Quran

2021

IEEE Access

View full text Add to dashboard Cite

In this paper, we first introduce the concept of T-spherical hesitant fuzzy set (T-SHFS) on the basis of the combination of T-spherical fuzzy sets(T-SFSs) and hesitant fuzzy sets (HFSs). This model can provide more accuracy in expressing fuzzy and indeterminate data. Then, we develop the basic operational laws of T-SHFSs and study their properties. Also, we propose the T-spherical hesitant fuzzy weighted averaging (T-SHFWA) operator and the T-spherical hesitant fuzzy weighted geometric (T-SHFWG) operatorand investigate their properties. Furthermore, two newly approaches to multi attribute decision making within the framework of T-SHFS are developed on the basis of the proposed aggregation operators. An illustrative example is given to demonstrate the effectiveness of the developed approaches in dealing with uncertain and indeterminate decision making problems. Finally, the proposed models' performance is evaluated to the existing models. Results from this comparison analysis reveal a great similarity and consistency while using other models.

show abstract

Section: The Comparison Analysis With the Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Multi Attribute Decision Making Method Based on the T-Spherical Hesitant Fuzzy Sets

Al-Quran

2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Furthermore, they introduced several novel aggregation operators to aggregate the q-RONF information, including the q-RONF weighted, the q-RONF hybrid operator and the q-RONF ordered weighted. Accordingly [63] measured the q-rung orthopair hesitant fuzzy sets)q-ROHFSs(and the properties related to the distance and similarity measures of q-ROHFSs, and the axiomatised definition and formula for the entropy of q-ROHFSs. Moreover, a q-rung orthopair shadowed set was suggested to represent attribute values and extends the vlsekriterijumska optimizcija i kaompromisno resenje (VIKOR) [50] .…”

Section: Introductionmentioning

confidence: 99%

Integration of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score methods under a q-rung orthopair environment: A distribution case study of COVID-19 vaccine doses

Albahri

Zaidan

et al. 2022

Computer Standards & Interfaces

View full text Add to dashboard Cite

Owing to the limitations of Pythagorean fuzzy and intuitionistic fuzzy sets, scientists have developed a distinct and successive fuzzy set called the q-rung orthopair fuzzy set (q-ROFS), which eliminates restrictions encountered by decision-makers in multicriteria decision making (MCDM) methods and facilitates the representation of complex uncertain information in real-world circumstances. Given its advantages and flexibility, this study has extended two considerable MCDM methods the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) under the fuzzy environment of q-ROFS. The extensions were called q-rung orthopair fuzzy-weighted zero-inconsistency (q-ROFWZIC) method and q-rung orthopair fuzzy decision by opinion score method (q-ROFDOSM). The methodology formulated had two phases. The first phase ‘development’ presented the sequential steps of each method thoroughly.The q-ROFWZIC method was formulated and used in determining the weights of evaluation criteria and then integrated into the q-ROFDOSM for the prioritisation of alternatives on the basis of the weighted criteria. In the second phase, a case study regarding the MCDM problem of coronavirus disease 2019 (COVID-19) vaccine distribution was performed. The purpose was to provide fair allocation of COVID-19 vaccine doses. A decision matrix based on an intersection of ‘recipients list’ and ‘COVID-19 distribution criteria’ was adopted. The proposed methods were evaluated according to systematic ranking assessment and sensitivity analysis, which revealed that the ranking was subject to a systematic ranking that is supported by high correlation results over different scenarios with variations in the weights of criteria.

show abstract

“…Chen and Tsao [39] suggested the TOPSIS technique based on interval-valued fuzzy information and addressed the experimental results. e authors in [40] established the extended TOPSIS method for q-ROHFSs and addressed their significance in DM. Li [41] proposed a TOPSIS-based nonlinear programming technique for MADM with interval-valued IFs in order to deal with uncertainty in real-world DM problems.…”

Section: Introductionmentioning

confidence: 99%

A Decision‐Making Framework Using q‐Rung Orthopair Probabilistic Hesitant Fuzzy Rough Aggregation Information for the Drug Selection to Treat COVID‐19

et al. 2022

View full text Add to dashboard Cite

In our current era, a new rapidly spreading pandemic disease called coronavirus disease (COVID-19), caused by a virus identified as a novel coronavirus (SARS-CoV-2), is becoming a crucial threat for the whole world. Currently, the number of patients infected by the virus is expanding exponentially, but there is no commercially available COVID-19 medication for this pandemic. However, numerous antiviral drugs are utilized for the treatment of the COVID-19 disease. Identification of the appropriate antivirus medicine to treat the infection of COVID-19 is still a complicated and uncertain decision. This study’s key objective is to develop a novel approach called q-rung orthopair probabilistic hesitant fuzzy rough set (q-ROPHFRS), which incorporates the q-rung orthopair fuzzy set, probabilistic hesitant fuzzy set, and rough set structures. New q-ROPHFR aggregation operators have been established: the q-ROPHFR Einstein weighted averaging (q-ROPHFREWA) operator and the q-ROPHFR Einstein weighted geometric (q-ROPHFREWG) operator. In this study, we explored some basic features of the developed operators. Afterward, to demonstrate the viability and feasibility of the established decision-making approach in real-world applications, a case study related to selecting drugs for COVID-19 pandemic is addressed. Furthermore, a comprehensive comparison with the q-rung orthopair probabilistic hesitant fuzzy rough TOPSIS technique is also presented to illustrate the benefits of the new framework. The obtained results confirm the reliability and effectiveness of the proposed approach for finding uncertainty in real-world decision-making.

show abstract

The Extension of TOPSIS Method for Multi-Attribute Decision-Making With q-Rung Orthopair Hesitant Fuzzy Sets

Cited by 25 publications

References 55 publications

A New Multi Attribute Decision Making Method Based on the T-Spherical Hesitant Fuzzy Sets

A New Multi Attribute Decision Making Method Based on the T-Spherical Hesitant Fuzzy Sets

Integration of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score methods under a q-rung orthopair environment: A distribution case study of COVID-19 vaccine doses

A Decision‐Making Framework Using q‐Rung Orthopair Probabilistic Hesitant Fuzzy Rough Aggregation Information for the Drug Selection to Treat COVID‐19

Contact Info

Product

Resources

About