Nonlinear integer goal programming models for acceptance sampling
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 aver...
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| Veröffentlicht in: | Computers & operations research 1986, Vol.13 (5), p.611-622 |
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| container_title | Computers & operations research |
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| creator | Ravindran, A. Shin, Wan Seon Arthur, Jeffrey L. Moskowitz, Herbert |
| description | 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. |
| doi_str_mv | 10.1016/0305-0548(86)90054-7 |
| format | Article |
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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.</description><identifier>ISSN: 0305-0548</identifier><identifier>EISSN: 1873-765X</identifier><identifier>EISSN: 0305-0548</identifier><identifier>DOI: 10.1016/0305-0548(86)90054-7</identifier><identifier>CODEN: CMORAP</identifier><language>eng</language><publisher>Oxford: Elsevier Ltd</publisher><subject>Acceptance sampling ; Algorithms ; Applied sciences ; Criteria ; Exact sciences and technology ; Goal programming ; Integer programming ; Mathematical models ; Multiple ; Nonlinear programming ; Operational research and scientific management ; Operational research. Management science ; Problem solving ; Reliability theory. 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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.</description><subject>Acceptance sampling</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Criteria</subject><subject>Exact sciences and technology</subject><subject>Goal programming</subject><subject>Integer programming</subject><subject>Mathematical models</subject><subject>Multiple</subject><subject>Nonlinear programming</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Problem solving</subject><subject>Reliability theory. 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Replacement problems</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ravindran, A.</creatorcontrib><creatorcontrib>Shin, Wan Seon</creatorcontrib><creatorcontrib>Arthur, Jeffrey L.</creatorcontrib><creatorcontrib>Moskowitz, Herbert</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Computers & operations research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ravindran, A.</au><au>Shin, Wan Seon</au><au>Arthur, Jeffrey L.</au><au>Moskowitz, Herbert</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonlinear integer goal programming models for acceptance sampling</atitle><jtitle>Computers & operations research</jtitle><date>1986</date><risdate>1986</risdate><volume>13</volume><issue>5</issue><spage>611</spage><epage>622</epage><pages>611-622</pages><issn>0305-0548</issn><eissn>1873-765X</eissn><eissn>0305-0548</eissn><coden>CMORAP</coden><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.</abstract><cop>Oxford</cop><pub>Elsevier Ltd</pub><doi>10.1016/0305-0548(86)90054-7</doi><tpages>12</tpages></addata></record> |
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| ispartof | Computers & operations research, 1986, Vol.13 (5), p.611-622 |
| issn | 0305-0548 1873-765X 0305-0548 |
| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Acceptance sampling Algorithms Applied sciences Criteria Exact sciences and technology Goal programming Integer programming Mathematical models Multiple Nonlinear programming Operational research and scientific management Operational research. Management science Problem solving Reliability theory. Replacement problems |
| title | Nonlinear integer goal programming models for acceptance sampling |
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