{"title":"基于agent的单点库存系统仿真模型","authors":"DONG Fu-gui, LIU Hui-mei, LU Bing-de","doi":"10.1016/j.sepro.2011.11.079","DOIUrl":null,"url":null,"abstract":"<div><p>Maintaining normal stock amount can reduce ordering cost and improve service level, but excessive stock needs expensive inventory holding costs and occupies too much floating capital, so it is necessary to seek a balance between the stock holdings and inventory cost. Using AnyLogic software, the single point inventory system simulation model is built based on Agent method in this paper. Through comparing the two continuous replenishment strategies, the (R, S) and (Q, R) strategies, the simulation results show that (R, S) strategy is better than (Q, R) strategy. Then the optimal inventory policy to minimize inventory cost with a certain service level is analyzed by the optimization experiment.</p></div>","PeriodicalId":101207,"journal":{"name":"Systems Engineering Procedia","volume":"4 ","pages":"Pages 298-304"},"PeriodicalIF":0.0000,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.sepro.2011.11.079","citationCount":"12","resultStr":"{\"title\":\"Agent-based Simulation Model of Single Point Inventory System\",\"authors\":\"DONG Fu-gui, LIU Hui-mei, LU Bing-de\",\"doi\":\"10.1016/j.sepro.2011.11.079\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Maintaining normal stock amount can reduce ordering cost and improve service level, but excessive stock needs expensive inventory holding costs and occupies too much floating capital, so it is necessary to seek a balance between the stock holdings and inventory cost. Using AnyLogic software, the single point inventory system simulation model is built based on Agent method in this paper. Through comparing the two continuous replenishment strategies, the (R, S) and (Q, R) strategies, the simulation results show that (R, S) strategy is better than (Q, R) strategy. Then the optimal inventory policy to minimize inventory cost with a certain service level is analyzed by the optimization experiment.</p></div>\",\"PeriodicalId\":101207,\"journal\":{\"name\":\"Systems Engineering Procedia\",\"volume\":\"4 \",\"pages\":\"Pages 298-304\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.sepro.2011.11.079\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Systems Engineering Procedia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2211381911002323\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Systems Engineering Procedia","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211381911002323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Agent-based Simulation Model of Single Point Inventory System
Maintaining normal stock amount can reduce ordering cost and improve service level, but excessive stock needs expensive inventory holding costs and occupies too much floating capital, so it is necessary to seek a balance between the stock holdings and inventory cost. Using AnyLogic software, the single point inventory system simulation model is built based on Agent method in this paper. Through comparing the two continuous replenishment strategies, the (R, S) and (Q, R) strategies, the simulation results show that (R, S) strategy is better than (Q, R) strategy. Then the optimal inventory policy to minimize inventory cost with a certain service level is analyzed by the optimization experiment.