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Titlebook: Generalized Concavity in Fuzzy Optimization and Decision Analysis; Jaroslav Ramík,Milan Vlach Book 2002 Springer Science+Business Media Ne

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樓主: Optician
21#
發(fā)表于 2025-3-25 04:38:20 | 只看該作者
Book 2002 extremely useful in the analysis and solution ofoptimization problems, including problems of either single objectiveor multiple objectives. Not all of these results rely necessarily onconvexity and concavity; some of the results can guarantee that eachlocal optimum is also a global optimum, giving
22#
發(fā)表于 2025-3-25 08:49:40 | 只看該作者
23#
發(fā)表于 2025-3-25 12:47:56 | 只看該作者
24#
發(fā)表于 2025-3-25 16:43:53 | 只看該作者
25#
發(fā)表于 2025-3-25 21:05:31 | 只看該作者
Kommunikation auf Anwendungsebene. is a function we need not only the underlying set . and its subset . but also one additional set, in this case the set {0, 1} or any other two-element set. Moreover, we also need the notion of Cartesian product because functions are specially structured binary relations, in this case special subsets of . × {0, 1}.
26#
發(fā)表于 2025-3-26 01:30:28 | 只看該作者
Technikgestaltung aus Frauenperspektiveria decision making, functions mapping the set of feasible alternatives into the unit interval [0, 1] of real numbers representing normalized utility functions can be interpreted as membership functions of fuzzy subsets of the underlying set of alternatives. However, functions with the range in [0, 1] arise in more contexts.
27#
發(fā)表于 2025-3-26 05:08:12 | 只看該作者
28#
發(fā)表于 2025-3-26 10:35:58 | 只看該作者
29#
發(fā)表于 2025-3-26 14:23:32 | 只看該作者
Fuzzy Multi-Criteria Decision Makingria decision making, functions mapping the set of feasible alternatives into the unit interval [0, 1] of real numbers representing normalized utility functions can be interpreted as membership functions of fuzzy subsets of the underlying set of alternatives. However, functions with the range in [0, 1] arise in more contexts.
30#
發(fā)表于 2025-3-26 20:49:41 | 只看該作者
Fuzzy Sequencing and SchedulingNevertheless, the resulting mathematical and computational problems often have the form of the following optimization problem: Given a finite number of jobs to be processed on a finite number of machines, find a feasible schedule that minimizes the value of a given objective function.
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