Download Fuzzy Stochastic Multiobjective Programming by Masatoshi Sakawa PDF

By Masatoshi Sakawa

Although experiences on multiobjective mathematical programming less than uncertainty were gathered and several other books on multiobjective mathematical programming less than uncertainty were released (e.g., Stancu-Minasian (1984); Slowinski and Teghem (1990); Sakawa (1993); Lai and Hwang (1994); Sakawa (2000)), there seems no booklet which matters either randomness of occasions on the topic of environments and fuzziness of human judgments concurrently in multiobjective determination making difficulties. during this publication, the authors are excited by introducing the newest advances within the box of multiobjective optimization below either fuzziness and randomness at the foundation of the authors’ carrying on with learn works. specified rigidity is put on interactive determination making facets of fuzzy stochastic multiobjective programming for human-centered structures below uncertainty in so much lifelike occasions while facing either fuzziness and randomness. association of every bankruptcy is in brief summarized as follows:

Chapter 2 is dedicated to mathematical preliminaries, in an effort to be used during the remainder

of the publication. beginning with uncomplicated notions and techniques of multiobjective programming, interactive

fuzzy multiobjective programming in addition to fuzzy multiobjective programming is outlined.

In bankruptcy three, via contemplating the imprecision of selection maker’s (DM’s) judgment for stochastic

objective features and/or constraints in multiobjective difficulties, fuzzy multiobjective stochastic

programming is constructed.

In bankruptcy four, during the attention of not just the randomness of parameters concerned in

objective services and/or constraints but in addition the specialists’ ambiguous realizing of the discovered values of the random parameters, multiobjective programming issues of fuzzy random variables are formulated.

In bankruptcy five, for resolving clash of choice making difficulties in hierarchical managerial or

public businesses the place there exist DMs who've various priorities in making judgements, two-level programming difficulties are mentioned.

Finally, bankruptcy 6 outlines a few destiny learn directions.

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Extra info for Fuzzy Stochastic Multiobjective Programming

Sample text

First, we give the notion of optimality in a multiobjective linear programming problem. Because there does not always exist a solution minimizing all of the objective functions simultaneously, the solution concept of Pareto optimality plays an important role in multiobjective optimization, and it is defined as follows. , X {x ∈ Rn | Ax ≤ b, x ≥ 0}. 6 (Pareto optimal solution). A point x∗ ∈ X is said to be a Pareto optimal solution if and only if there does not exist another x ∈ X such that zi (x) ≤ zi (x∗ ) for all i ∈ {1, .

Then, the objective function to be minimized may be the expectation of cx + q+ y+ + q− y− . 43) Ax ≤ b ⎪ ⎪ ⎭ x ≥ 0, where E means the function of expectation. 43) is called a simple recourse problem. 43) unless qi + q− i is negative, and therefore we assume − + q ≥ 0, i = 1, . . 44) ⎪ ⎪ ∑ ti j x j , ⎪ ⎭ j=1 where ti j is the i j-element of T . Assume that the random variables d¯i , i = 1, . . , m are independent each other. 43) as follows: E q+ y+ + q− y− m =∑ i=1 m q+ i ∞ ∑nj=1 ti j x j n m j=1 i=1 n di − ∑ ti j x j dFi (di ) + ∑ n m − + ¯ = ∑ q+ i E[di ] − ∑ ti j x j + ∑ (qi + qi ) i=1 j=1 ∑nj=1 ti j x j q− i −∞ ∑ ti j x j i=1 n ∑ ti j x j − di n Fi j=1 ∑ ti j x j j=1 ∑nj=1 ti j x j m − − ∑ (q+ i + qi ) −∞ i=1 dFi (di ) j=1 di dFi (di ).

88) is solved. 90) Ax + By ≤ b ⎪ ⎪ ⎭ y ≥ 0, uT ≥ 0, vT ≥ 0, where u is an m dimensional row vector and v is an n2 dimensional row vector. 91) ⎪ ⎪ Ax + By ≤ b ⎪ ⎪ ⎭ x ≥ 0, y ≥ 0, uT ≥ 0, vT ≥ 0. 91), v is eliminated and the equality constraint u(Ax + By − b) − vy = 0 is transformed into u(b − Ax − By) + (uB + d2 )y = 0. 92) implies that b − Ax − By ≥ 0, uT ≥ 0, (uB+d2 )T ≥ 0, y ≥ 0. Let Ai and Bi be the ith row vector of the matrix A and the matrix B, respectively, and let B j and d2 j be the jth column vector of the matrix B and the jth element of the vector d2 , respectively.

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