Abstract
Two lexicographic goal programming models are developed for determining the optimal sample size and acceptance number for acceptance sampling plans in quality control. Both models address the conflicting criteria inherent in such sampling problems, namely the average lot inspection cost and the average outgoing quality. The first model assumes a known constant lot fraction defective, while the second relaxes this assumption and instead assumes knowledge of a prior distribution on the fraction of defectives. A three-phase algorithm is developed which exploits the problem structure in order to find optimal solutions after examining a small percentage of the feasible sampling plans. On a set of 64 test problems the algorithm always found the optimal solution, typically after evaluating only 3-5% (and never more than 9%) of the feasible points.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 611-622 |
| Number of pages | 12 |
| Journal | Computers and Operations Research |
| Volume | 13 |
| Issue number | 5 |
| DOIs | |
| State | Published - Jan 1 1986 |
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All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Modeling and Simulation
- Management Science and Operations Research
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Nonlinear integer goal programming models for acceptance sampling. / Ravindran, Arunachalam; Shin, Wan Seon; Arthur, Jeffrey L.; Moskowitz, Herbert.
In: Computers and Operations Research, Vol. 13, No. 5, 01.01.1986, p. 611-622.Research output: Contribution to journal › Article
TY - JOUR
T1 - Nonlinear integer goal programming models for acceptance sampling
AU - Ravindran, Arunachalam
AU - Shin, Wan Seon
AU - Arthur, Jeffrey L.
AU - Moskowitz, Herbert
PY - 1986/1/1
Y1 - 1986/1/1
N2 - Two lexicographic goal programming models are developed for determining the optimal sample size and acceptance number for acceptance sampling plans in quality control. Both models address the conflicting criteria inherent in such sampling problems, namely the average lot inspection cost and the average outgoing quality. The first model assumes a known constant lot fraction defective, while the second relaxes this assumption and instead assumes knowledge of a prior distribution on the fraction of defectives. A three-phase algorithm is developed which exploits the problem structure in order to find optimal solutions after examining a small percentage of the feasible sampling plans. On a set of 64 test problems the algorithm always found the optimal solution, typically after evaluating only 3-5% (and never more than 9%) of the feasible points.
AB - Two lexicographic goal programming models are developed for determining the optimal sample size and acceptance number for acceptance sampling plans in quality control. Both models address the conflicting criteria inherent in such sampling problems, namely the average lot inspection cost and the average outgoing quality. The first model assumes a known constant lot fraction defective, while the second relaxes this assumption and instead assumes knowledge of a prior distribution on the fraction of defectives. A three-phase algorithm is developed which exploits the problem structure in order to find optimal solutions after examining a small percentage of the feasible sampling plans. On a set of 64 test problems the algorithm always found the optimal solution, typically after evaluating only 3-5% (and never more than 9%) of the feasible points.
UR - http://www.scopus.com/inward/record.url?scp=0022889080&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0022889080&partnerID=8YFLogxK
U2 - 10.1016/0305-0548(86)90054-7
DO - 10.1016/0305-0548(86)90054-7
M3 - Article
AN - SCOPUS:0022889080
VL - 13
SP - 611
EP - 622
JO - Surveys in Operations Research and Management Science
JF - Surveys in Operations Research and Management Science
SN - 0305-0548
IS - 5
ER -