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

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p1=p1dash/onerat p2=p2dash/tworat p3=p3dash/threerat p=(p1+p2+p3) ‘Set the Gamma1 and Gamma2 Values gamma1=1.0 gamma2=0.7 ‘Initialize for Minregret () For k = 0 To 50 MINREGRET(i)=10000 Next ‘Set Lotsize Lot=1000 ‘Start iteration w.r.t. c 3 For c 3 =0 To 30 ‘Calculate lower bound and upper bound for the sample size for a given c 3 nupbound=(Log(gamma2)+((c3+1)*Log(onerat))/(pdash-p)) n1pbound=(Log(gamma2)+(c3*Log(threerat))/(pdash-p)) ‘Start iteration w.r.t c 1 ,c 2 For c2=0 To c3 For c1=0 To c2 ‘PrevRegret is a variable used to limit the n value not to exceed the optimum n. To start with we set a large value for PrevRegret prevRegret=10000 For n = n1pbound To nupbound Step 2 ‘Calculate Regret using the Pa function to obtain Probability of <strong>acceptance</strong> Regret = n+(lot – n)*(gamma1*(1-PA(c1, c2, c3, n, p1, p2, p3)) + gamma2* PA(c1, c2, c3, n, p1dash, p2dash, p3dash)) RegretprevRegret Then GoTo 100 ‘n need not be incremented beyond this point 119
If Regret,OMREGRET(c3-1) Tjem Prevoptc3=c3-1 ‘Go to print the output sheet GoTo 200 End If Next c3 200 ‘Print output Sheet Worksheets(“sheet1”).Cells(2+j,1).Value=lot Worksheets(“sheet1”).Cells(2+j,2).Value=prevoptc3 Worksheets(“sheet1”).Cells(2+j,3).Value=optc2(prevoptc3) Worksheets(“sheet1”).Cells(2+j,4).Value=optc1(prevoptc3) Worksheets(“sheet1”).Cells(2+j,5).Value=optn(prevoptc3) Worksheets(“sheet1”).Cells(2+j,6).Value=MINREGRET(prevoptc3) 120
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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
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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 and 122: Table 2.5.2 : Optimal multi attribu
Page 123: Annexure Microsoft Visual Basic pro
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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