{"title":"随机需求下供应链成本优化的随机模型预测控制","authors":"Tikito Kawtar, S. Achchab, Y. Benadada","doi":"10.1109/GOL.2014.6887436","DOIUrl":null,"url":null,"abstract":"This paper aims to present a Stochastic Model Predictive Control to optimize the costs of shipping and storage in a supply chain. The goal is to minimize the objective function of the combined costs in a multi-stage and a multi-level supply chain responding to a stochastic multi-product demand. The simulation using Matlab provides a comparison between classical models and the proposed model, and shows that the Affine Recourse Stochastic Model Predictive Control - AR SMPC offers better results.","PeriodicalId":265851,"journal":{"name":"2014 International Conference on Logistics Operations Management","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Stochastic Model Predictive Control for costs optimization in a supply chain under a stochastic demand\",\"authors\":\"Tikito Kawtar, S. Achchab, Y. Benadada\",\"doi\":\"10.1109/GOL.2014.6887436\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper aims to present a Stochastic Model Predictive Control to optimize the costs of shipping and storage in a supply chain. The goal is to minimize the objective function of the combined costs in a multi-stage and a multi-level supply chain responding to a stochastic multi-product demand. The simulation using Matlab provides a comparison between classical models and the proposed model, and shows that the Affine Recourse Stochastic Model Predictive Control - AR SMPC offers better results.\",\"PeriodicalId\":265851,\"journal\":{\"name\":\"2014 International Conference on Logistics Operations Management\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 International Conference on Logistics Operations Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GOL.2014.6887436\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference on Logistics Operations Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GOL.2014.6887436","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Stochastic Model Predictive Control for costs optimization in a supply chain under a stochastic demand
This paper aims to present a Stochastic Model Predictive Control to optimize the costs of shipping and storage in a supply chain. The goal is to minimize the objective function of the combined costs in a multi-stage and a multi-level supply chain responding to a stochastic multi-product demand. The simulation using Matlab provides a comparison between classical models and the proposed model, and shows that the Affine Recourse Stochastic Model Predictive Control - AR SMPC offers better results.