Decision Making with Dominance Constraints in Two-Stage by Uwe Gotzes PDF

By Uwe Gotzes

ISBN-10: 3834808431

ISBN-13: 9783834808431

ISBN-10: 3834899917

ISBN-13: 9783834899910

Two-stage stochastic programming versions are regarded as beautiful instruments for making optimum judgements less than uncertainty. regularly, optimality is formalized by means of making use of statistical parameters resembling the expectancy or the conditional price in danger to the distributions of aim values.

Uwe Gotzes analyzes an method of account for chance aversion in two-stage types dependent upon partial orders at the set of genuine random variables. those stochastic orders allow the incorporation of the features of entire distributions into the choice procedure. The revenue or fee distributions needs to move a benchmark attempt with a given appropriate distribution. hence, extra pursuits could be optimized. For this new classification of stochastic optimization difficulties, effects on constitution and balance are confirmed and a adapted set of rules to take on huge challenge cases is built. the results of the modelling historical past and numerical effects from the applying of the proposed set of rules are tested with case reports from power buying and selling.

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Extra info for Decision Making with Dominance Constraints in Two-Stage Stochastic Integer Programming

Example text

K we arrive at the following Lagrangean function (see, e. , [81, 101]) × RK+ L : X × RL×K + −→ R (x1 , . . , xL , Δ, λ ) −→ L ∑π =1 · L (x , Δ • , λ ), where Δ • denotes the -th row of Δ and K L (x , Δ • , λ ) := gT x + ∑ λk · (v k − E [(a − ak )+ ]) , k=1 for = 1, . . , L. 1) with g x replaced with the Lagrangean L (x1 , . . 18 Since we are keeping track of finding a lower bound as large as possible, we are interested in the solution of the Lagrangean dual. This amounts to max{D(λ ) : λ ≥ 0}, where ⎫ ⎪ D(λ ) := min L (x1 , .

And For ∈ I, choose y ∈ Y such that q y = Φ(z − T x) and T x +Wy = z . Then c x + q y − ak = fx (z ) − ak = v , yielding x ∈ S2 . For S2 ⊂ S1 let x ∈ S2 and (v )L=1 be a feasible configuration of the v . Consider I := ∈ {1, . . , L} : v > 0 . The definition of S2 implies that for ∈ I there exist y ∈ Y fulfilling c x + q y − ak ≤ 0 T x +Wy = z . and Therefore, fx (z ) − ak ≤ 0 for all ∈ I. For ∈ I there exist y ∈ Y with c x + q y − ak ≤ v T x +Wy = z . and Thus, fx (z ) − ak ≤ v for all ∈ I. Now we obtain Rs [ fx (ζ ) − ak ]+ μ(dζ ) = (∗) ≤ ∑ π [ fx (z ) − ak ]+ + ∑ π [ fx (z ) − ak ]+ ∈I L ∑π v =1 ∈I (x∈S2 ) ≤ R [a − ak ]+ ν(da), (∗) holds because ∀ ∈ / I : [ fx (z ) − ak ]+ = 0 and because π v ≥ 0, So x ∈ S1 , and the proof is complete.

L of first-stage decisions from the lower bounding procedure just described 2: Output: An upper bound for the current node, or just the information, that the current node has to be branched further 3: Understand x , = 1, . . 1). For instance, this can be done by averaging and rounding where required (cf. [54], where lots of details regarding the implementation of the algorithms are explained). 4: for = 1 to L do 4 Decomposition Method 5: 55 if K min ∑vk : k=1 6: 7: 8: 9: 10: 11: 12: 13: c x¯ + q y − v k ≤ ak T x¯ +Wy = z y ∈ Y, v k ≥ 0, k = 1, .

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Decision Making with Dominance Constraints in Two-Stage Stochastic Integer Programming by Uwe Gotzes

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