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

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Table 1.2.5 (contd.) : Three attribute A kind single sampling plans for normal inspection p ( ) /Total SL. n.(AQL 1 ) n.(AQL 2 ) n.(AQL 3 ) a 1 a 2 a 3 Over all Prod. 0. 10 No. risk AQL 40 0.3155 0.5 3.155 4 4 7 0.050 3 41 0.7924 1.256 1.991 3 5 8 0.038 3.2 42 0.1256 0.7924 3.155 1 3 8 0.040 3.2 43 0.1991 0.7924 3.155 2 3 8 0.040 3.1 44 0.3155 0.7924 3.155 3 3 8 0.050 3 45 0.5 0.7924 3.155 3 4 8 0.044 2.9 46 0.1256 1.256 3.155 2 4 8 0.050 2.8 47 0.1991 1.256 3.155 2 5 8 0.048 2.8 48 0.3155 1.256 3.155 2 4 9 0.041 3 49 0.5 1.256 3.155 2 5 9 0.044 2.9 50 0.7924 1.256 3.155 4 5 9 0.050 2.7 51 0.1256 1.991 3.155 2 6 9 0.045 2.7 52 0.1256 0.1991 5 2 2 9 0.049 2.7 53 0.1991 1.991 3.155 2 6 9 0.049 2.7 54 0.1256 0.3155 5 1 2 10 0.037 2.8 55 0.3155 1.991 3.155 2 5 10 0.048 2.8 56 0.1991 0.3155 5 1 2 10 0.050 2.8 57 0.1256 0.5 5 1 3 10 0.038 2.7 58 0.5 1.991 3.155 2 6 10 0.048 2.7 59 0.1991 0.5 5 1 3 10 0.050 2.7 60 0.3155 0.5 5 2 3 10 0.045 2.6 61 0.1256 0.7924 5 2 3 10 0.050 2.6 62 0.7924 1.991 3.155 3 7 10 0.048 2.6 63 0.1991 0.7924 5 2 4 10 0.045 2.6 64 0.3155 0.7924 5 3 5 10 0.048 2.5 65 0.5 0.7924 5 2 4 11 0.045 2.6 66 0.1256 1.256 5 1 4 11 0.046 2.6 67 1.256 1.991 3.155 4 7 11 0.045 2.6 68 0.1991 1.256 5 2 4 11 0.045 2.6 69 0.3155 1.256 5 2 5 11 0.042 2.5 70 0.5 1.256 5 3 5 11 0.049 2.4 71 0.7924 1.256 5 3 5 12 0.046 2.5 72 0.1256 1.991 5 2 5 12 0.045 2.5 73 0.1991 1.991 5 3 5 12 0.050 2.4 74 0.3155 1.991 5 2 6 12 0.044 2.4 75 0.5 1.991 5 5 6 12 0.050 2.4 76 0.7924 1.991 5 3 6 13 0.050 2.4 77 1.256 1.991 5 5 8 13 0.045 2.3 78 0.1256 0.1991 7.924 2 2 13 0.046 2.3 57
Table 1.2.5 (contd.): Three attribute A kind single sampling plans for normal inspection p ( ) /Total SL. n.(AQL 1 ) n.(AQL 2 ) n.(AQL 3 ) a 1 a 2 a 3 Over all Prod. 0. 10 No. risk AQL 79 0.1256 3.155 5 2 8 13 0.046 2.3 80 0.1991 3.155 5 2 8 13 0.050 2.3 81 0.1256 0.3155 7.924 2 3 13 0.047 2.3 82 0.1991 0.3155 7.924 2 4 13 0.050 2.2 83 0.3155 3.155 5 2 7 14 0.047 2.3 84 0.1256 0.5 7.924 1 3 14 0.038 2.3 85 0.1991 0.5 7.924 1 3 14 0.049 2.3 86 0.5 3.155 5 2 8 14 0.050 2.3 87 0.3155 0.5 7.924 2 3 14 0.043 2.3 88 0.1256 0.7924 7.924 2 3 14 0.048 2.3 89 0.1991 0.7924 7.924 2 4 14 0.042 2.3 90 0.7924 3.155 5 3 9 14 0.049 2.2 91 0.3155 0.7924 7.924 2 4 14 0.049 2.2 92 0.5 0.7924 7.924 4 6 14 0.049 2.2 93 0.1256 1.256 7.924 1 4 15 0.045 2.3 94 0.1991 1.256 7.924 2 4 15 0.044 2.3 95 1.256 3.155 5 4 9 15 0.045 2.2 96 0.3155 1.256 7.924 2 5 15 0.040 2.2 97 0.5 1.256 7.924 3 5 15 0.046 2.2 98 0.7924 1.256 7.924 4 7 15 0.049 2.1 99 0.1256 1.991 7.924 2 5 16 0.045 2.2 100 0.1991 1.991 7.924 2 5 16 0.049 2.2 101 1.991 3.155 5 5 10 16 0.049 2.2 102 0.3155 1.991 7.924 2 6 16 0.042 2.2 103 0.5 1.991 7.924 3 6 16 0.048 2.1 104 0.7924 1.991 7.924 4 8 16 0.048 2.1 105 1.256 1.991 7.924 5 7 17 0.049 2.1 106 0.1256 3.155 7.924 1 8 17 0.047 2.1 107 0.1991 3.155 7.924 2 8 17 0.044 2.1 108 0.3155 3.155 7.924 3 8 17 0.048 2.1 109 0.5 3.155 7.924 3 10 17 0.050 2 110 0.7924 3.155 7.924 4 8 18 0.048 2.1 111 1.256 3.155 7.924 6 10 18 0.049 2 112 0.1256 0.1991 12.56 1 2 19 0.049 2 113 0.1256 0.3155 12.56 1 3 19 0.050 2 114 0.1256 5 7.924 2 11 19 0.047 2 115 1.991 3.155 7.924 6 11 19 0.050 2 116 0.1991 0.3155 12.56 2 3 19 0.047 2 117 0.1991 5 7.924 2 11 19 0.050 2 58
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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
Page 11 and 12: procurement of materials of the US
Page 13 and 14: average defective from such a proce
Page 15 and 16: 0.4 Scope of the present inquiry 0.
Page 17 and 18: 0.4.6 A generalized acceptance samp
Page 19 and 20: sembled units the general practice
Page 21 and 22: with the above purpose in mind. The
Page 23 and 24: equals to p i in the long run. We a
Page 25 and 26: m j , r∏ −P C j ′ = g(x j , m
Page 27 and 28: show that if we increase c 2 keepin
Page 29 and 30: independence and mutually exclusive
Page 31 and 32: different sampling schemes in terms
Page 33 and 34: curves i.e. the OC curve as a funct
Page 35 and 36: 0.5.3 Part3 Bayesian multiattribute
Page 37 and 38: K(N, n)/(A 1 − R 1 ) = nk ′ s +
Page 39 and 40: example, a sample of finished garme
Page 41 and 42: ...(1.1.2) If now X i is assumed to
Page 43 and 44: (i) Poisson as approximation to bin
Page 45 and 46: 1.2 Multiattribute sampling schemes
Page 47 and 48: We take this case and the case of t
Page 49 and 50: highest with respect to critical de
Page 51 and 52: ...(1.2.4) Note that, for single at
Page 53 and 54: ...(1.2.7) To compare the relative
Page 55 and 56: Corollary For ρ 2 /ρ 1 > c 2 /c 1
Page 57 and 58: increasing in each a i . Note that
Page 59 and 60: Figure 1.2.1 Absolute value of Slop
Page 61: Table 1.2.5: Three attribute A kind
Page 65 and 66: Table 1.2.5 (contd.) : Three attrib
Page 67 and 68: Table 1.2.5 (contd.) : Three attrib
Page 69 and 70: 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
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where a 0 and n 0 are the parameter
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where, p = (p 1 , p 2 , ..., p r )
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situations as above. For the third
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2.5.3 Optimal Bayesian plans in sit
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Table 2.5.2 : Optimal multi attribu
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Annexure Microsoft Visual Basic pro
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If Regret,OMREGRET(c3-1) Tjem Prevo
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3.1 Bayesian single sampling multia
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f(p i , ¯p i , s i )dp i = e −v
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easonable to assume that the p i
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the minimum cost is obtained at a 1
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Table 3.1.1 (Contd.) : Results of 1
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Table 3.1.2 : Testing goodness of f
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Figure 3.1.1 : Scatter plots of num
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Figure 3.1.2: Sketch of the cost fu
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Annexure Microsoft Visual Basic pro
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References Ailor, R. B., Schmidt, J
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Copenhagen. Hamaker, H. C. (1950).
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Taylor, E. F. (1957). Discovery sam
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