{"title":"质量指标AOQL和MAPD的最优抽样方案","authors":"R. Balan, E. Massawe","doi":"10.14738/tmlai.105.13168","DOIUrl":null,"url":null,"abstract":"This paper describes a selection procedure for an Optimum Sampling Plan, offering maximum consumer protection in terms of AOQL and MAPD. The greatest lower bound (glb) property of AOQL for a fixed MAPD is used to design the plan offering highest precision on outgoing quality for the lot. Tables for optimum sampling plans corresponding to specified MAPD and g l b of AOQL are listed along with AQL. Empirical relation to determine AOQL for given acceptance number and MAPD is determined. Also an approximated acceptance number function in terms of (MAPD, AOQL) is developed. Lower and Upper bounds of AOQL for some parametric sampling plans are listed.","PeriodicalId":119801,"journal":{"name":"Transactions on Machine Learning and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimum Sampling Plan on Quality Indices AOQL and MAPD\",\"authors\":\"R. Balan, E. Massawe\",\"doi\":\"10.14738/tmlai.105.13168\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes a selection procedure for an Optimum Sampling Plan, offering maximum consumer protection in terms of AOQL and MAPD. The greatest lower bound (glb) property of AOQL for a fixed MAPD is used to design the plan offering highest precision on outgoing quality for the lot. Tables for optimum sampling plans corresponding to specified MAPD and g l b of AOQL are listed along with AQL. Empirical relation to determine AOQL for given acceptance number and MAPD is determined. Also an approximated acceptance number function in terms of (MAPD, AOQL) is developed. Lower and Upper bounds of AOQL for some parametric sampling plans are listed.\",\"PeriodicalId\":119801,\"journal\":{\"name\":\"Transactions on Machine Learning and Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transactions on Machine Learning and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14738/tmlai.105.13168\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transactions on Machine Learning and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14738/tmlai.105.13168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimum Sampling Plan on Quality Indices AOQL and MAPD
This paper describes a selection procedure for an Optimum Sampling Plan, offering maximum consumer protection in terms of AOQL and MAPD. The greatest lower bound (glb) property of AOQL for a fixed MAPD is used to design the plan offering highest precision on outgoing quality for the lot. Tables for optimum sampling plans corresponding to specified MAPD and g l b of AOQL are listed along with AQL. Empirical relation to determine AOQL for given acceptance number and MAPD is determined. Also an approximated acceptance number function in terms of (MAPD, AOQL) is developed. Lower and Upper bounds of AOQL for some parametric sampling plans are listed.
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