By Baoding Liu (auth.)
Real-life judgements tend to be made within the kingdom of uncertainty corresponding to randomness and fuzziness. How will we version optimization difficulties in doubtful environments? How can we clear up those versions? with the intention to resolution those questions, this e-book presents a self-contained, complete and up to date presentation of doubtful programming thought, together with quite a few modeling principles, hybrid clever algorithms, and purposes in process reliability layout, venture scheduling challenge, car routing challenge, facility situation challenge, and computing device scheduling challenge. Researchers, practitioners and scholars in operations study, administration technology, info technological know-how, approach technology, and engineering will locate this paintings a stimulating and worthy reference.
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Theory And Practice Of Uncertain Programming
Real-life judgements are typically made within the nation of uncertainty comparable to randomness and fuzziness. How will we version optimization difficulties in doubtful environments? How will we resolve those versions? to be able to solution those questions, this booklet offers a self-contained, entire and up to date presentation of doubtful programming idea, together with a variety of modeling rules, hybrid clever algorithms, and functions in approach reliability layout, undertaking scheduling challenge, car routing challenge, facility position challenge, and laptop scheduling challenge.
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3. It is obvious that E ∗ ⊂ E ∗∗ holds. 21. The jth constraint gj (x, ξ) ≤ 0 is called a dependent constraint of the event E if the set of nondegenerate decision variables of gj (x, ξ) and the dependent support E ∗∗ have nonempty intersection; otherwise it is independent. 4. An active constraint must be a dependent constraint. 22. Let E be an event hk (x, ξ) ≤ 0, k = 1, 2, · · · , q in the uncertain environment gj (x, ξ) ≤ 0, j = 1, 2, · · · , p. For each decision x and realization ξ, the event E is said to be consistent in the uncertain environment if the following two conditions hold: (i) hk (x, ξ) ≤ 0, k = 1, 2, · · · , q; and (ii) gj (x, ξ) ≤ 0, j ∈ J, where J is the index set of all dependent constraints.
3. Then the outputs of the neurons in the hidden layer are y1 y2 y3 ... ... ......... .......... ........... ....... ...... ...... .. ... . ..... ... .. .... ...... ................... ....................... . ............................... . . . . . . . . . . . .... ............ ..... ............................... .... .................. ... ........ ... . ... ... .... ............ ... ............. ................... ... ........ ..........