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 118 0.1256 0.5 12.56 2 4 19 0.048 2 119 0.3155 5 7.924 3 13 19 0.050 2 120 0.1991 0.5 12.56 1 3 20 0.049 2 121 0.3155 0.5 12.56 2 3 20 0.043 2 122 0.5 5 7.924 4 10 20 0.050 2 123 0.1256 0.7924 12.56 2 3 20 0.047 2 124 0.1991 0.7924 12.56 2 4 20 0.040 2 125 0.3155 0.7924 12.56 2 4 20 0.046 2 126 0.7924 5 7.924 4 11 20 0.049 2 127 0.5 0.7924 12.56 3 5 20 0.046 2 128 0.1256 1.256 12.56 2 5 20 0.049 1.9 129 0.1991 1.256 12.56 2 6 20 0.050 1.9 130 0.3155 1.256 12.56 2 5 21 0.039 2 131 1.256 5 7.924 6 11 21 0.049 2 132 0.5 1.256 12.56 3 5 21 0.043 2 133 0.7924 1.256 12.56 4 6 21 0.047 1.9 134 0.1256 1.991 12.56 2 6 21 0.048 1.9 135 0.1991 1.991 12.56 2 7 21 0.048 1.9 136 0.3155 1.991 12.56 3 8 21 0.050 1.9 137 1.991 5 7.924 6 12 22 0.050 1.9 138 0.5 1.991 12.56 3 6 22 0.045 1.9 139 0.7924 1.991 12.56 4 7 22 0.046 1.9 140 1.256 1.991 12.56 5 7 23 0.047 1.9 141 0.1256 3.155 12.56 2 7 23 0.048 1.9 142 0.1991 3.155 12.56 2 8 23 0.041 1.9 143 0.3155 3.155 12.56 2 8 23 0.047 1.9 144 3.155 5 7.924 9 14 23 0.049 1.9 145 0.5 3.155 12.56 3 9 23 0.045 1.9 146 0.7924 3.155 12.56 5 11 23 0.049 1.8 147 1.256 3.155 12.56 7 9 24 0.050 1.9 148 0.1256 5 12.56 2 10 25 0.048 1.8 149 1.991 3.155 12.56 7 10 25 0.049 1.8 150 0.1991 5 12.56 2 11 25 0.044 1.8 151 0.3155 5 12.56 2 11 25 0.050 1.8 152 0.5 5 12.56 3 12 25 0.049 1.8 153 0.7924 5 12.56 4 11 26 0.046 1.8 154 1.256 5 12.56 5 13 26 0.048 1.8 155 1.991 5 12.56 7 13 27 0.049 1.8 156 0.1256 0.1991 19.91 1 2 28 0.048 1.8 59
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 157 0.1256 0.3155 19.91 1 3 28 0.048 1.8 158 0.1991 0.3155 19.91 2 3 28 0.045 1.8 159 0.1256 0.5 19.91 2 3 28 0.049 1.8 160 0.1991 0.5 19.91 2 4 28 0.048 1.8 161 0.1256 7.924 12.56 2 16 28 0.048 1.8 162 0.1991 7.924 12.56 3 16 28 0.050 1.7 163 3.155 5 12.56 10 17 28 0.050 1.7 164 0.3155 0.5 19.91 3 5 28 0.050 1.7 165 0.3155 7.924 12.56 3 14 29 0.048 1.8 166 0.1256 0.7924 19.91 2 3 29 0.047 1.8 167 0.1991 0.7924 19.91 2 4 29 0.039 1.8 168 0.5 7.924 12.56 3 15 29 0.045 1.8 169 0.3155 0.7924 19.91 2 4 29 0.045 1.8 170 0.5 0.7924 19.91 3 5 29 0.044 1.8 171 0.7924 7.924 12.56 4 16 29 0.048 1.7 172 0.1256 1.256 19.91 2 5 29 0.046 1.8 173 0.1991 1.256 19.91 2 5 29 0.049 1.7 174 0.3155 1.256 19.91 3 6 29 0.048 1.7 175 0.5 1.256 19.91 3 5 30 0.043 1.8 176 1.256 7.924 12.56 4 16 30 0.050 1.8 177 0.7924 1.256 19.91 4 6 30 0.044 1.7 178 0.1256 1.991 19.91 2 6 30 0.046 1.7 179 0.1991 1.991 19.91 2 6 30 0.049 1.7 180 0.3155 1.991 19.91 3 7 30 0.047 1.7 181 0.5 1.991 19.91 4 8 30 0.050 1.7 182 1.991 7.924 12.56 7 16 31 0.050 1.7 183 0.7924 1.991 19.91 4 7 31 0.044 1.7 184 1.256 1.991 19.91 5 9 31 0.049 1.7 185 0.1256 3.155 19.91 2 9 31 0.049 1.7 186 0.1991 3.155 19.91 3 10 31 0.050 1.7 187 0.3155 3.155 19.91 2 8 32 0.045 1.7 188 0.5 3.155 19.91 3 8 32 0.049 1.7 189 3.155 7.924 12.56 9 18 32 0.050 1.7 190 0.7924 3.155 19.91 4 9 32 0.050 1.7 191 1.256 3.155 19.91 5 9 33 0.049 1.7 192 0.1256 5 19.91 2 10 34 0.047 1.7 193 1.991 3.155 19.91 6 10 34 0.049 1.7 194 0.1991 5 19.91 2 10 34 0.050 1.7 195 0.3155 5 19.91 2 11 34 0.047 1.7 60
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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
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 and 62: Table 1.2.5: Three attribute A kind
Page 63: Table 1.2.5 (contd.): Three attribu
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
Page 113 and 114: 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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