{"title":"基于聚类搜索算法的制造单元的形成","authors":"P.H. Gu, H.A. ElMaraghy","doi":"10.1016/0378-3804(89)90048-X","DOIUrl":null,"url":null,"abstract":"<div><p>This paper presents three cluster-seeking algorithms - K-means, Revised K-means and Isodata - for formation of part families and machine cells. These algorithms are based on the concept of pattern recognition and are capable of producing variable size, mutually independent groups of parts and/or machines without excluding exceptional components. These algorithms are compared with existing grouping algorithms, and examples are used to demonstrate the effect of clustering criteria on the final solutions. It has been found that the Isodata algorithm is more efficient and more flexible than existing machine/components matrix manipulation techniques.</p></div>","PeriodicalId":100801,"journal":{"name":"Journal of Mechanical Working Technology","volume":"20 ","pages":"Pages 403-413"},"PeriodicalIF":0.0000,"publicationDate":"1989-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/0378-3804(89)90048-X","citationCount":"5","resultStr":"{\"title\":\"Formation of manufacturing cells by cluster-seeking algorithms\",\"authors\":\"P.H. Gu, H.A. ElMaraghy\",\"doi\":\"10.1016/0378-3804(89)90048-X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper presents three cluster-seeking algorithms - K-means, Revised K-means and Isodata - for formation of part families and machine cells. These algorithms are based on the concept of pattern recognition and are capable of producing variable size, mutually independent groups of parts and/or machines without excluding exceptional components. These algorithms are compared with existing grouping algorithms, and examples are used to demonstrate the effect of clustering criteria on the final solutions. It has been found that the Isodata algorithm is more efficient and more flexible than existing machine/components matrix manipulation techniques.</p></div>\",\"PeriodicalId\":100801,\"journal\":{\"name\":\"Journal of Mechanical Working Technology\",\"volume\":\"20 \",\"pages\":\"Pages 403-413\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1989-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/0378-3804(89)90048-X\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mechanical Working Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/037838048990048X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mechanical Working Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/037838048990048X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Formation of manufacturing cells by cluster-seeking algorithms
This paper presents three cluster-seeking algorithms - K-means, Revised K-means and Isodata - for formation of part families and machine cells. These algorithms are based on the concept of pattern recognition and are capable of producing variable size, mutually independent groups of parts and/or machines without excluding exceptional components. These algorithms are compared with existing grouping algorithms, and examples are used to demonstrate the effect of clustering criteria on the final solutions. It has been found that the Isodata algorithm is more efficient and more flexible than existing machine/components matrix manipulation techniques.