Nonlinear integer goal programming models for acceptance sampling

A. Ravindran, Wan Seon Shin, Jeffrey L. Arthur, Herbert Moskowitz

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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 languageEnglish (US)
Pages (from-to)611-622
Number of pages12
JournalComputers and Operations Research
Issue number5
StatePublished - 1986

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research


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