{"title":"井服公司资源调度的遗传算法","authors":"A. Brezulianu, L. Fira, M. Fira","doi":"10.14569/IJARAI.2012.010501","DOIUrl":null,"url":null,"abstract":"In this paper, two examples of resources scheduling in well-services companies are solved by means of genetic algorithms: resources for call solving, people scheduling. The results demonstrate that the genetic algorithm approach can provide acceptable solutions to this type of call solving for scheduling in well-services companies. The suggested approach could be easily extended to various resource scheduling areas: process scheduling, transportation scheduling.","PeriodicalId":323606,"journal":{"name":"International Journal of Advanced Research in Artificial Intelligence","volume":"123 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A genetic algorithm approach for scheduling of resources in well-services companies\",\"authors\":\"A. Brezulianu, L. Fira, M. Fira\",\"doi\":\"10.14569/IJARAI.2012.010501\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, two examples of resources scheduling in well-services companies are solved by means of genetic algorithms: resources for call solving, people scheduling. The results demonstrate that the genetic algorithm approach can provide acceptable solutions to this type of call solving for scheduling in well-services companies. The suggested approach could be easily extended to various resource scheduling areas: process scheduling, transportation scheduling.\",\"PeriodicalId\":323606,\"journal\":{\"name\":\"International Journal of Advanced Research in Artificial Intelligence\",\"volume\":\"123 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Advanced Research in Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14569/IJARAI.2012.010501\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Advanced Research in Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14569/IJARAI.2012.010501","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A genetic algorithm approach for scheduling of resources in well-services companies
In this paper, two examples of resources scheduling in well-services companies are solved by means of genetic algorithms: resources for call solving, people scheduling. The results demonstrate that the genetic algorithm approach can provide acceptable solutions to this type of call solving for scheduling in well-services companies. The suggested approach could be easily extended to various resource scheduling areas: process scheduling, transportation scheduling.
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