{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T13:44:25Z","timestamp":1777902265845,"version":"3.51.4"},"reference-count":32,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2013,4,19]],"date-time":"2013-04-19T00:00:00Z","timestamp":1366329600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["SIMULATION"],"published-print":{"date-parts":[[2013,7]]},"abstract":"<jats:p>Fuzzy Monte Carlo Simulation (FMCS) uses both the probability density function ( pdf) and possibility distributions (e.g., fuzzy numbers) to model the uncertainty\/imprecision associated with the input parameters and, then, to simulate the uncertainty\/imprecision associated with the output parameters. A probability\u2013possibility transformation is needed to transfer the information of a fuzzy number into its equivalent pdf, while performing the simulation. This study deals with an approach of FMCS that uses a point-cloud-based probability\u2013possibility transformation. Let x( t), t = 0,1,\u2026, n, be a set of points that represents some random states of an uncertain\/imprecise quantity. The collection of points ( x( t), x( t+ i)), t = 0,\u2026, n\u2013 i, i\u2208 {1,2,\u2026} is called point-cloud, providing a visual\/computational representation of variability, modality, and ranges associated with the quantity. This study identifies the pdf and possibility distribution (fuzzy number) underlying a given point-cloud. Using these distributions, the relationships between the triangular fuzzy number and unimodal pdf (normal\/uniform distributions) are identified. Two numerical examples are described elucidating the effectiveness of the proposed transformation. The first example deals with the issue of monitoring a FMCS process. The other example deals with the issue of making a decision by using FMCS.<\/jats:p>","DOI":"10.1177\/0037549713482174","type":"journal-article","created":{"date-parts":[[2013,4,19]],"date-time":"2013-04-19T23:20:52Z","timestamp":1366413652000},"page":"860-875","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":31,"title":["Fuzzy Monte Carlo Simulation using point-cloud-based probability\u2013possibility transformation"],"prefix":"10.1177","volume":"89","author":[{"given":"A M M","family":"Sharif Ullah","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Kitami Institute of Technology, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M","family":"Shamsuzzaman","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, College of Engineering, University of Sharjah, 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