Credibilistic Programming: An Introduction to Models and Applications (Uncertainty and Operations Research)

It offers fuzzy programming method of remedy real-life selection difficulties in fuzzy atmosphere. in the framework of credibility thought, it presents a self-contained, accomplished and up to date presentation of fuzzy programming types, algorithms and functions in portfolio research.

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36 2 Credibilistic Programming 2. 2 Credibilistic Programming Fuzzy programming is the mathematical programming in fuzzy setting, that's, the target functionality f or constraint capabilities gi , i = 1, 2, . . . , n include fuzzy parameters. think that x is a choice vector, and ξ is a fuzzy vector, then the final fuzzy programming version should be written as max f (x, ξ ) s. t. gi (x, ξ ) ≤ zero, i = 1, 2, . . . , n. (2. nine) instance 2. four during this instance, we give some thought to the portfolio choice challenge. The time period portfolio refers to any number of monetary resources corresponding to shares, bonds, and money. Portfolio could be held by means of person traders or controlled by way of monetary execs, banks and different monetary associations. imagine that there are m shares, and we use ξi to indicate the go back of the ith inventory. usually, ξi is given as (pi + di − pi )/pi the place pi is the final expense at the moment, pi is the ultimate cost within the subsequent yr, and di is the dividend through the coming 12 months. observe that the values of pi and di in a destiny period of time are truly unknown at this time. in the event that they are anticipated as fuzzy amounts, then ξi is a fuzzy variable. additionally, for every portfolio (x1 , x2 , . . . , xm ), the place xi denotes the share of the whole capital invested in inventory i, the whole go back f (x, ξ ) = ξ1 x1 + ξ2 x2 + · · · + ξm xm is additionally a fuzzy variable. therefore, if the investor want to maximize the complete go back, we get the next fuzzy programming version ⎧ ⎪ ⎨ max ξ1 x1 + ξ2 x2 + · · · + ξm xm s. t. x1 + x2 + · · · + xm = 1 (2. 10) ⎪ ⎩ xi ≥ zero, i = 1, 2, . . . , m the place the 1st constraint signifies that all of the capital may be invested to the m shares, and the subsequent set of constraints means that brief sale and borrowing aren't allowed. as a rule talking, it really is meaningless to maximise a fuzzy target when you consider that there's not a ordinary ordership in fuzzy global. for this reason, we have to outline a credibilistic mapping from the gathering of fuzzy variables to the set of genuine numbers, such that we will be able to rank fuzzy variables in keeping with the average ordership of genuine numbers. For the bushy programming version (2. 9), if the credibilistic mappings U, U1 , U2 , . . . , Un are taken, we get the subsequent version max U f (x, ξ ) s. t. Ui gi (x, ξ ) ≤ zero, i = 1, 2, . . . , n. (2. eleven) notice that (2. eleven) is a crisp nonlinear programming version because the aim functionality and constraints are either good outlined. In what follows, we'll name it a credibilistic programming version. the subsequent chapters will introduce a few as a rule used 2. 2 Credibilistic Programming 37 credibilistic mappings together with the anticipated price operator, positive price, pessimistic price, entropy, cross-entropy, and distance. Definition 2. 6 For the credibilistic programming version (2. 11), the set S= x∈ m | Ui gi (x, ξ ) ≤ zero, i = 1, 2, . . . , n (2. 12) is named the possible set. a component x in S is named a possible answer. Definition 2. 7 For the credibilistic programming version (2. 11), a possible resolution x ∗ is named the neighborhood optimum answer if and provided that there's a actual quantity ε > zero such that U f x ∗, ξ ≥ U f (x, ξ ) (2.

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