Optimization of fuzzy relational equations with convex combination of max-t-norm and max-average compositions -t-norm - * (*: ykw@vnu.edu.tw) View all notes 3201

摘要

An optimization model with a linear objective function subject to fuzzy relational equations with a linear convex combination of max-min and max-average compositions has been presented in the literature. This study extends the same optimization model to a generalized fuzzy operator structured by the linear convex combination of max-t-norm and max-average compositions. Although many different results built on the max-t-norm composition, such as max-min and max-product compositions, are proposed, the properties generated from this convex combination operator are similar to the max-average composition, but different with max-min composition. Moreover, these properties enable us to convert the linear optimization problem into the set covering problem and solve by the 0-1 integer programming problem with simplified model. Numerical examples illustrate the extended results and solution procedures. (fuzzy relational equations)-- -t-norm - -t-norm -- - - (set covering problem) 0-1 View full textDownload full textKeywordsfuzzy optimization, fuzzy relational equations, max-t-norm composition, max-average composition, set covering problemKeywords -t-norm - Related var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10170669.2010.514124