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

More documents

Recommendations

Info

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 196 0.5 5 19.91 3 11 34 0.050 1.7 197 5 7.924 12.56 12 21 34 0.048 1.7 198 0.7924 5 19.91 4 13 34 0.049 1.7 199 1.256 5 19.91 5 12 35 0.048 1.7 200 1.991 5 19.91 6 13 36 0.048 1.7 201 0.1256 7.924 19.91 2 15 37 0.046 1.7 202 0.1991 7.924 19.91 2 15 37 0.048 1.6 203 3.155 5 19.91 9 15 37 0.049 1.6 204 0.3155 7.924 19.91 3 16 37 0.047 1.6 205 0.5 7.924 19.91 4 17 37 0.049 1.6 206 0.7924 7.924 19.91 4 15 38 0.050 1.6 207 1.256 7.924 19.91 6 17 38 0.049 1.6 208 1.991 7.924 19.91 6 18 39 0.050 1.6 209 3.155 7.924 19.91 10 21 40 0.050 1.6 210 0.1256 0.1991 31.55 2 3 41 0.049 1.6 211 0.1256 0.3155 31.55 2 2 42 0.046 1.6 212 0.1991 0.3155 31.55 2 3 42 0.040 1.6 213 0.1256 0.5 31.55 1 3 42 0.049 1.6 214 0.1991 0.5 31.55 2 3 42 0.046 1.6 215 0.3155 0.5 31.55 2 4 42 0.047 1.6 216 0.1256 0.7924 31.55 2 4 42 0.046 1.6 217 0.1991 0.7924 31.55 2 4 42 0.049 1.6 218 0.1256 12.56 19.91 2 22 42 0.049 1.6 219 0.3155 0.7924 31.55 3 5 42 0.048 1.6 220 0.1991 12.56 19.91 3 22 42 0.050 1.6 221 0.3155 12.56 19.91 4 24 42 0.050 1.6 222 5 7.924 19.91 11 21 43 0.046 1.6 223 0.5 0.7924 31.55 3 4 43 0.046 1.6 224 0.1256 1.256 31.55 2 4 43 0.049 1.6 225 0.5 12.56 19.91 3 21 43 0.048 1.6 226 0.1991 1.256 31.55 2 5 43 0.043 1.6 227 0.3155 1.256 31.55 2 5 43 0.048 1.6 228 0.7924 12.56 19.91 4 22 43 0.048 1.6 229 0.5 1.256 31.55 3 6 43 0.046 1.6 230 0.7924 1.256 31.55 5 7 43 0.049 1.6 231 0.1256 1.991 31.55 3 8 43 0.050 1.6 232 1.256 12.56 19.91 5 22 44 0.046 1.6 233 0.1991 1.991 31.55 2 6 44 0.044 1.6 234 0.3155 1.991 31.55 2 6 44 0.049 1.6 61
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 235 0.5 1.991 31.55 3 7 44 0.046 1.6 236 0.7924 1.991 31.55 4 8 44 0.049 1.6 237 1.991 12.56 19.91 8 25 44 0.050 1.6 238 1.256 1.991 31.55 5 8 45 0.045 1.6 239 0.1256 3.155 31.55 2 8 45 0.045 1.6 240 0.1991 3.155 31.55 2 8 45 0.048 1.6 241 0.3155 3.155 31.55 2 9 45 0.049 1.6 242 0.5 3.155 31.55 4 9 45 0.049 1.6 243 0.7924 3.155 31.55 3 9 46 0.050 1.6 244 3.155 12.56 19.91 9 24 46 0.050 1.6 245 1.256 3.155 31.55 5 10 46 0.049 1.6 246 0.1256 5 31.55 2 11 47 0.046 1.6 247 1.991 3.155 31.55 6 11 47 0.049 1.6 248 0.1991 5 31.55 2 11 47 0.049 1.6 249 0.3155 5 31.55 3 12 47 0.047 1.6 250 0.5 5 31.55 3 13 47 0.050 1.5 251 0.7924 5 31.55 4 12 48 0.044 1.6 252 5 12.56 19.91 11 27 48 0.050 1.6 253 1.256 5 31.55 6 13 48 0.049 1.5 254 1.991 5 31.55 7 14 49 0.047 1.5 255 0.1256 7.924 31.55 2 16 50 0.049 1.5 256 0.1991 7.924 31.55 3 16 50 0.050 1.5 257 3.155 5 31.55 9 17 50 0.050 1.5 258 0.3155 7.924 31.55 3 18 50 0.050 1.5 259 0.5 7.924 31.55 3 15 51 0.049 1.5 260 0.7924 7.924 31.55 4 16 51 0.049 1.5 261 7.924 12.56 19.91 17 31 51 0.049 1.5 262 1.256 7.924 31.55 7 22 51 0.050 1.5 263 1.991 7.924 31.55 7 19 52 0.050 1.5 264 3.155 7.924 31.55 8 19 54 0.049 1.5 265 0.1256 12.56 31.55 3 24 55 0.050 1.5 266 0.1991 12.56 31.55 2 21 56 0.046 1.5 267 0.3155 12.56 31.55 3 21 56 0.047 1.5 268 5 7.924 31.55 12 21 56 0.049 1.5 269 0.5 12.56 31.55 3 22 56 0.048 1.5 270 0.7924 12.56 31.55 4 23 56 0.050 1.5 271 1.256 12.56 31.55 6 22 57 0.049 1.5 272 1.991 12.56 31.55 7 23 58 0.048 1.5 273 3.155 12.56 31.55 9 25 59 0.049 1.5 62
Page 1 and 2:
Multiattribute acceptance sampling
Page 3 and 4:
e) the cost functions are linear an
Page 5 and 6:
Contents 0.1 Purpose of sampling in
Page 7 and 8:
Part 0 : Introduction 0.1 Purpose o
Page 9 and 10:
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 and 62: Table 1.2.5: Three attribute A kind
Page 63 and 64: Table 1.2.5 (contd.): Three attribu
Page 65: 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
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 and 124:
Annexure Microsoft Visual Basic pro
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
show all

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

Create successful ePaper yourself

Delete template?

Save as template?