{"title":"供应链大爆炸的规划:生产指标的快速对冲","authors":"D. L. Woodruff, S. Voß","doi":"10.1109/HICSS.2006.380","DOIUrl":null,"url":null,"abstract":"We concern ourselves with the process of making optimized production planning decisions in the face of low frequency, high impact uncertainty, which takes the form of a small number of discrete scenarios. Computational results provide evidence that the computational effort for the full stochastic mixed integer problem can be reduced by first solving scenario sub-problems and then blending them to find values for some of the binary variables.","PeriodicalId":432250,"journal":{"name":"Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2006-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Planning for a Big Bang in a Supply Chain: Fast Hedging for Production Indicators\",\"authors\":\"D. L. Woodruff, S. Voß\",\"doi\":\"10.1109/HICSS.2006.380\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We concern ourselves with the process of making optimized production planning decisions in the face of low frequency, high impact uncertainty, which takes the form of a small number of discrete scenarios. Computational results provide evidence that the computational effort for the full stochastic mixed integer problem can be reduced by first solving scenario sub-problems and then blending them to find values for some of the binary variables.\",\"PeriodicalId\":432250,\"journal\":{\"name\":\"Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)\",\"volume\":\"20 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-01-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HICSS.2006.380\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HICSS.2006.380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Planning for a Big Bang in a Supply Chain: Fast Hedging for Production Indicators
We concern ourselves with the process of making optimized production planning decisions in the face of low frequency, high impact uncertainty, which takes the form of a small number of discrete scenarios. Computational results provide evidence that the computational effort for the full stochastic mixed integer problem can be reduced by first solving scenario sub-problems and then blending them to find values for some of the binary variables.