Multiattribute acceptance sampling plans - Library(ISI Kolkata ...

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Table 2.5.3 : Optimal multiattribute Bayesian <strong>plans</strong> for two point discrete prior Distribution r = 3 , p ' /p = 8, p ' /p = 5, p ' /p = 3, p' = 0.01, p' = 0.04, p' = 0.10, γ = 1.0, γ = 1 1 2 2 3 3 1 2 3 1 2 0.7 Optimal A plan Optimal D Plan Optimal C plan Lot Size a 1 a 2 a 3 n Regret k n Regret c 3 c 2 c 1 n Regret 1000 3 4 9 106 149.16 8 97 149.86 8 3 1 112 176.07 2000 3 5 11 130 186.09 11 133 186.83 11 4 2 158 226.34 3000 4 6 13 155 207.76 13 155 208.67 13 5 2 189 255.87 4000 4 7 15 177 223.73 14 168 224.62 13 5 2 191 279.44 5000 4 7 15 179 235.91 15 179 236.85 15 6 3 224 295.66 6000 4 7 16 189 245.75 16 190 246.70 16 6 3 235 310.04 7000 4 8 17 202 254.10 17 202 255.09 17 7 3 256 321.57 8000 4 8 18 212 261.54 18 213 262.67 17 7 3 257 331.33 9000 4 8 18 212 267.93 18 214 268.97 18 7 3 267 339.87 10000 4 9 19 224 274.02 19 225 274.97 18 7 3 268 348.19 117
Annexure Microsoft Visual Basic programme as Excel Macro “OptimalAPlan()” for obtaining the Bayesian Optimal A plan for discrete prior Distribution for a given lot size, and values of ( p , p , p ), ( p′ , p′ , p′ ) , γ , γ 1 2 3 2 2 3 1 2 ‘The following array variables are used ; ‘MINREGRET () is an array variable denoting the regret value for a given c 3 ‘Optc2() is an array variable denoting the optimum c 2 value for a given c 3 ‘Optc1() is an array variable denoting the optimum c 1 value for a given c 3 Optcn() is an array variable denoting the optimum n value for a given c 3 ‘For all other variables see the explanations given as comment Sub optimalAthree() Dim MINREGRET(50) Dim optc2(50) Dim optc1(50) Dim optn(50) J = 1 ‘j is a variable used to define the row of the output sheet ‘Print Header for the output worksheet “Sheet 1” Worksheets(“sheet1”).Cells(1,1).Value=”LOT” Worksheets(“Sheet1”).Cells(1,2).Value=”Optimum c 3 ” Worksheets(“Sheet1”).Cells(1,3).Value=”Optimum c 2 ” Worksheets(“Sheet1”).Cells(1,4).Value=”Optimum c1” Worksheets(“Sheet1”).Cells(1,5).Value=”Optimum n” Worksheets(“sheet1”).Cells(1,6).Value=”Regret Value” ‘Set the process average values at stage 2 for the first, second and the third attribute p1dash=1/100 p2dash=4/100 p3dash=10/100 pdash =p1dash+p2dash+p3dash ‘Set the ratios of the process averages at stage two and stage one for attribute 1,2 and three onerat=8 tworat=5 threerat=3 118
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Multiattribute acceptance sampling
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e) the cost functions are linear an
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Contents 0.1 Purpose of sampling in
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Part 0 : Introduction 0.1 Purpose o
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ectification is defined by setting
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procurement of materials of the US
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average defective from such a proce
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0.4 Scope of the present inquiry 0.
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0.4.6 A generalized acceptance samp
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sembled units the general practice
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with the above purpose in mind. The
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equals to p i in the long run. We a
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m j , r∏ −P C j ′ = g(x j , m
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show that if we increase c 2 keepin
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independence and mutually exclusive
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different sampling schemes in terms
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curves i.e. the OC curve as a funct
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0.5.3 Part3 Bayesian multiattribute
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K(N, n)/(A 1 − R 1 ) = nk ′ s +
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example, a sample of finished garme
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...(1.1.2) If now X i is assumed to
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(i) Poisson as approximation to bin
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1.2 Multiattribute sampling schemes
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We take this case and the case of t
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highest with respect to critical de
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...(1.2.4) Note that, for single at
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...(1.2.7) To compare the relative
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Corollary For ρ 2 /ρ 1 > c 2 /c 1
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increasing in each a i . Note that
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Figure 1.2.1 Absolute value of Slop
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Table 1.2.5: Three attribute A kind
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Table 1.2.5 (contd.): Three attribu
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Table 1.2.5 (contd.) : Three attrib
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Table 1.2.5 (contd.) : Three attrib
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1.3 Multiattribute sampling plans o
Page 71 and 72: Let M 2 < M 1 . Then G(c 1 , M 1 ρ
Page 73 and 74: α, β, and ρ. For α = 0.05, β =
Page 75 and 76: ma β (a 1 , a 2 , ρ) = m ′ ...(
Page 77 and 78: Table:1.3.2: Construction parameter
Page 79 and 80: 1.3.5 D kind plans of given strengt
Page 81 and 82: Figure 1.3.1: The sketch of the fun
Page 83 and 84: 2.1 General cost models 2.1.1 Scope
Page 85 and 86: (j) Interaction of scrappable attri
Page 87 and 88: use of defective item is additive o
Page 89 and 90: 2.1.5 Approximation under Poisson c
Page 91 and 92: 2.2 The expected cost model for dis
Page 93 and 94: P (p (j) ) denotes the probability
Page 95 and 96: 250 200 Figure 2.2.1 : Lot Quality
Page 97 and 98: Table 2. 2. 1 Testing goodness of f
Page 99 and 100: 2. 3 Cost of MASSP's of A kind 2.3.
Page 101 and 102: From (2.3.2) we observe that γ 2 1
Page 103 and 104: ( γ / ) 2 γ − ( m′− m e ) /
Page 105 and 106: We now consider the following funct
Page 107 and 108: ….(2.3.12) Here S A denotes the s
Page 109 and 110: ∴There exists an optimal A plan f
Page 111 and 112: Hence, Q(m) = 1 − P (m) has the s
Page 113 and 114: where a 0 and n 0 are the parameter
Page 115 and 116: where, p = (p 1 , p 2 , ..., p r )
Page 117 and 118: situations as above. For the third
Page 119 and 120: 2.5.3 Optimal Bayesian plans in sit
Page 121: Table 2.5.2 : Optimal multi attribu
Page 125 and 126: If Regret,OMREGRET(c3-1) Tjem Prevo
Page 127 and 128: 3.1 Bayesian single sampling multia
Page 129 and 130: f(p i , ¯p i , s i )dp i = e −v
Page 131 and 132: easonable to assume that the p i
Page 133 and 134: the minimum cost is obtained at a 1
Page 135 and 136: Table 3.1.1 (Contd.) : Results of 1
Page 137 and 138: Table 3.1.2 : Testing goodness of f
Page 139 and 140: Figure 3.1.1 : Scatter plots of num
Page 141 and 142: Figure 3.1.2: Sketch of the cost fu
Page 143 and 144: Annexure Microsoft Visual Basic pro
Page 145 and 146: References Ailor, R. B., Schmidt, J
Page 147 and 148: Copenhagen. Hamaker, H. C. (1950).
Page 149: Taylor, E. F. (1957). Discovery sam
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