Incomplete pythagorean fuzzy preference relation for subway station safety management during COVID-19 pandemic

Zhang, Zhenyu; Zhang, Huirong; Zhou, Lixin; Qin, Yong; Jia, Limin

doi:10.1016/j.eswa.2022.119445

Cited by 8 publications

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References 60 publications

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“…Preference relationship (PR) is an important tool for experts to describe preference information after comparing alternatives. The traditional PR mainly characterizes the preference degree between alternatives on a scale of 1/9~9 [4] and has been widely used in economic, environmental, and risk management fields [5][6][7][8][9]. At present, the research on multiplicative PRs (MPRs) [10], additive PRs (APRs) [11], fuzzy PRs (FPRs) [12], and linguistic PRs (LPRs) [13] is complete.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiplicative Consistent q-Rung Orthopair Fuzzy Preference Relations with Application to Critical Factor Analysis in Crowdsourcing Task Recommendation

Yin,

Zhang

2023

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This paper presents a group decision-making (GDM) method based on q-rung orthopair fuzzy preference relations (q-ROFPRs). Firstly, the multiplicative consistent q-ROFPRs (MCq-ROFPRs) and the normalized q-rung orthopair fuzzy priority weight vectors (q-ROFPWVs) are introduced. Then, to obtain q-ROFPWVs, a goal programming model under q-ROFPRs is established to minimize their deviation from the MCq-ROFPRs and minimize the weight uncertainty. Further, a group goal programming model of ideal MCq-ROFPRs is constructed to obtain the expert weights using the compatibility measure between the ideal MCq-ROFPRs and the individual q-ROFPRs. Finally, a GDM method with unknown expert weights is solved by combining the group goal programming model and the simple q-rung orthopair fuzzy weighted geometric (Sq-ROFWG) operator. The effectiveness and practicality of the proposed GDM method are verified by solving the crucial factors in crowdsourcing task recommendation. The results show that the developed GDM method effectively considers the important measures of experts and identifies the crucial factors that are more reliable than two other methods.

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