Incomplete pythagorean fuzzy preference relation for subway station safety management during COVID-19 pandemic
Expert Systems with Applications, ISSN: 0957-4174, Vol: 216, Page: 119445
2023
- 11Citations
- 15Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Completing the Pythagorean fuzzy preference relations (PFPRs) based on additive consistency may exceed the defined domain. Therefore, we develop a group decision-making (GDM) method with incomplete PFPRs. Firstly, sufficient conditions for the expressibility of estimated preference values in PFPRs based on additive consistency are presented. Next, the correction algorithm is developed to correct the inexpressible elements in incomplete PFPRs. Then, a GDM method based on incomplete PFPRs is proposed to determine the objective weights of decision-makers. Finally, an example of subway station safety management during COVID-19 is selected to illustrate the applicability of the developed GDM method. The results show that the developed GDM method effectively identifies the crucial risk factor in subway station safety management and has better performance in terms of computational time complexity than the multiplicative consistency method.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0957417422024642; http://dx.doi.org/10.1016/j.eswa.2022.119445; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85144572047&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/36570381; https://linkinghub.elsevier.com/retrieve/pii/S0957417422024642; https://dx.doi.org/10.1016/j.eswa.2022.119445
Elsevier BV
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know