Bayes Markovian Decision Models for a Multistage Reject Allowance Problem
Bayes Markovian Decision Models for a Multistage Reject Allowance Problem
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In our eReader you can find the full English version of the book. Read Bayes Markovian Decision Models for a Multistage Reject Allowance Problem Online - link to read the book on full screen. Our eReader also allows you to upload and read Pdf, Txt, ePub and fb2 books. In the Mini eReder on the page below you can quickly view all pages of the book - Read Book Bayes Markovian Decision Models for a Multistage Reject Allowance Problem
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If i e T or x = 0, then c(i, x) also includes the cost of excess if i > I or shortage if i < I, and the remaining storage costs until the date of shipments The description of the fixed capacity problem is now complete. Using the results of Derman [2], we can formulate the problem as a linear programs - 9 - The solution to the linear program is then used to find the optimal decision rule. (As pointed out in [U], [8], the problem could also be formulated as a dynamic programs ) The linear program to be solved is given by: Minimize: Z = E I z(i, x)c(i, x) i X ^ ^ Subject to: z(j, x) - 0, j^ e S, x = O, l, „o, k-s; s s z(''0 - l E z(j^. X) - z(*) = 1 X s I z(j^, x) - i: z(i^, s-r) q^ (r, s-r) = 0, j - i» s - r, j^ e S-O^; I z(0, x) - E E z(i, x) = X i eT X ^ r T. Z(0^, x) = 1, X 0* The elements of the optimal decision rule are computed as d(i, x) =, . . , . , '^ T. Z(i, x) - 10 - If the denominator is zero then the value of d may be set arbitrarily without changing the expected minimum costo It might be assumed that prior to the start of production several different production capacities are available rather than just one» In this case, let K denote the set of capacities and C(K), Ke K » the costs related to operating at capacity Ko Then the optimal capacity K* could be determined by sowing the equation C(K''0 = Min {c(K) + Z(K)} KeK.
What to read after Bayes Markovian Decision Models for a Multistage Reject Allowance Problem?
You can find similar books in the "Read Also" column, or choose other free books by Leon S Leon Seligsberger White to read online
To quickly assess the difficulty of the text, read a short excerpt:
If i e T or x = 0, then c(i, x) also includes the cost of excess if i > I or shortage if i < I, and the remaining storage costs until the date of shipments The description of the fixed capacity problem is now complete. Using the results of Derman [2], we can formulate the problem as a linear programs - 9 - The solution to the linear program is then used to find the optimal decision rule. (As pointed out in [U], [8], the problem could also be formulated as a dynamic programs ) The linear program to be solved is given by: Minimize: Z = E I z(i, x)c(i, x) i X ^ ^ Subject to: z(j, x) - 0, j^ e S, x = O, l, „o, k-s; s s z(''0 - l E z(j^. X) - z(*) = 1 X s I z(j^, x) - i: z(i^, s-r) q^ (r, s-r) = 0, j - i» s - r, j^ e S-O^; I z(0, x) - E E z(i, x) = X i eT X ^ r T. Z(0^, x) = 1, X 0* The elements of the optimal decision rule are computed as d(i, x) =, . . , . , '^ T. Z(i, x) - 10 - If the denominator is zero then the value of d may be set arbitrarily without changing the expected minimum costo It might be assumed that prior to the start of production several different production capacities are available rather than just one» In this case, let K denote the set of capacities and C(K), Ke K » the costs related to operating at capacity Ko Then the optimal capacity K* could be determined by sowing the equation C(K''0 = Min {c(K) + Z(K)} KeK.
What to read after Bayes Markovian Decision Models for a Multistage Reject Allowance Problem?
You can find similar books in the "Read Also" column, or choose other free books by Leon S Leon Seligsberger White to read online
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