{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T18:11:33Z","timestamp":1769710293357,"version":"3.49.0"},"reference-count":40,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,7,2]]},"abstract":"<jats:p>In order to estimate the deficiency of a city in its ability to prevent and control risks, as well as to evaluate the corresponding measures, this paper focuses on multi-attribute decision making based on LINMAP method and Manhattan distance at linguistic q-rung orthopair fuzzy. Manhattan distance is a new product that combines clustering distance with linguistic q-rung orthopair fuzzy to be able to use the data more effectively for measurement. LINMAP method is a decision making method based on ideal points, which can solve the weights as well as provide ideal solutions by linear programming model. The combination of the two can create a new decision-making method, which can effectively evaluate the decision scheme of social public facilities according to the actual needs of decision-makers. The new method has the following advantages: (1) the conditions of linguistic fuzzy numbers can be applied more comprehensively, making the decision more realistic and effective; (2) the Manhattan distance is more in line with the human way of thinking and closer to life; (3) after comparative study, the results produced by this method have certain reliability.<\/jats:p>","DOI":"10.3233\/jifs-221750","type":"journal-article","created":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T15:51:32Z","timestamp":1683647492000},"page":"1341-1355","source":"Crossref","is-referenced-by-count":2,"title":["An approach to linguistic q-rung orthopair fuzzy multi-attribute decision making with LINMAP based on Manhattan distance measure"],"prefix":"10.1177","volume":"45","author":[{"given":"Pengcheng","family":"Gao","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Anhui University, Hefei, 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