By Hitoshi Yano
Recently, many books on multiobjective programming were released. even though, just a couple of books were released, within which multiobjective programming less than the randomness and the fuzziness are investigated. nevertheless, numerous books on multilevel programming were released, within which a number of determination makers are concerned with hierarchical choice occasions. during this ebook, we introduce the most recent advances within the box of multiobjective programming and multilevel programming less than uncertainty. The reader can instantly use proposed ways to resolve multiobjective programming and multilevel programming, that are according to linear programming or convex programming approach. association of every capter is summarized as follows. In bankruptcy 2, multiobjective programming issues of random variables are formulated, and the corresponding interactive algorithms are constructed to procure a passable answer, during which the fuzziness of human's subjective judgment for permission degrees are thought of. In bankruptcy three, multiobjective programming issues of fuzzy random variables are formulated, and the corresponding interactive algorithms are built to procure a passable resolution, within which not just the uncertainty of fuzzy random variables but additionally the fuzziness of human's subjective judgment for permission degrees are thought of. In bankruptcy four, multiobjective multilevel programming is mentioned, and the interactive algorithms are built to procure a passable resolution, within which the hierarchical determination constitution of a number of choice makers is mirrored. In bankruptcy five, sorts of farm making plans difficulties are solved by way of using the proposed procedure, during which fee coefficients of vegetation are expressed through random variables.
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Extra info for Interactive Multiobjective Decision Making under Uncertainty
160). 23 as follows. 82), respectively, and reference membership values μ^i, i = 1,2,3 are all set to one. 5. At optimal solution A, instead of obtaining worse permissible objective levels in comparison with optimal solution B, the corresponding objective function values fi(x*,p^i), i = 1,2,3 improve. At optimal solution C, instead of improving permissible probability levels in comparison with the optimal solution B, the corresponding objective function values fi(x*,p^i), i = 1,2,3 worsen. 160).
188) can be transformed into the following form. 189) as follows. 29(p^) can be equivalently reduced to the following simple form. 30(p^). 31 minx∈X,p^i∈(0,1),i=1,⋯,k(f1(x,p^1),⋯,fk(x,p^k),−p^1,⋯,−p^k) Here, permissible probability levels p^i, i = 1, ⋯, k are not fixed values, but rather decision variables. 31. Such a fuzzy goal can be quantified via the corresponding membership function. Let us denote a membership function of objective function fi(x,p^i) as μfi(fi(x,p^i)) and a membership function of permissible probability level p^i as μp^i(p^i) respectively.
16 can be transformed into the following form. 112) where Fi=def[fimin,fimax],i=1,⋯,k. 17, we next introduce an MP-Pareto optimal solution. 17 if and only if there does not exist another x∈X, f^i∈Fi,i=1,⋯,k such that μDpi(x,f^i)≥μDpi(x*,f^i*)i=1,⋯,k, with strict inequality holding for at least one i. To generate a candidate for a satisfactory solution that is also MP-Pareto optimal, the decision maker is asked to specify reference membership values . 113) are specified, the corresponding MP-Pareto optimal solution is obtained by solving the following minmax problem.
Interactive Multiobjective Decision Making under Uncertainty by Hitoshi Yano