TY - JOUR
T1 - Nonlinear integer goal programming models for acceptance sampling
AU - Ravindran, A.
AU - Shin, Wan Seon
AU - Arthur, Jeffrey L.
AU - Moskowitz, Herbert
N1 - Funding Information:
*A. Ravindran is Professor and Director of the School of Industrial Engineering at the University of Oklahoma, Norman. He received his B.S. in Electrical Engineering from Birla Institute of Technology and Science Pilani (India), and MS. and Ph.D. degrees in Industrial Engin~~ng and Operations Research from the University of California at Berkeley. He has coauthored two texts in Operations Research and has publish~ over 45 articles in the areas of math~atical programing, multicriteria optimization, goal programming, metalcutting, health planning,energy models, and transportation analysis. He served as the Director of the O.R. Division of the Institute of Industrial Engineers. He is a member of the Editorial Board of Computers and industrial Engineering and IEEE ‘l?ansactions on Engineering Management. i’Wan Seon Shin is a doctoral student in Industrial Ennineerina at the Universitv of Oklahoma. He received his undergraduate degree in Industrial Engineering at the Han Yang Univ&sity, Seoul, Korea: and his MSIE at the University of Oklahoma. His current research interest is in the theory and application of multiple criteria optimization. SJeffrey L. Arthur is an Associate Professor of Operations Research in the Department of Statistics at Oregon State University. He received his Ph.D. in Industrial Engineering and Operations Research from Pu.due University. He serves on the Editorial Board of IEEE 7kmsactions on Engineering Management. gHerbert Moskowitz Ph.D. University of California at Los Angeles, is a Professor of Management and Director, Professional Graduate Programs in Management, at the Krannert Graduate School of Management, Purdue University. He has authored four books in management science and statistics and has published some 60 papers in such journals as ~~~agemenr Science, Operatives Research, AIIE Tra~act~o~s, Decision Sciences, Omega, Academy qf ,~uffugement, iEEE ~ansactialls on E~~neeri~Ig ~~u~agernent, Policy Science and Orgff~iz~tional Behavior and Human Performance. He is a Fellow and Secretary of the American Institute for Decision Sciences, has served as vice-president and member of the Executive Board. and is the ORSA liaison representative to this organization. He is also a member of TIMS. Professor Moskowitz has been awarded a Fulbright Grant for 1985-1986 under the Western European Regional Research Scholar Program.
PY - 1986
Y1 - 1986
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.
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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 - Computers and Operations Research
JF - Computers and Operations Research
SN - 0305-0548
IS - 5
ER -