{"title":"电子商务环境下的快递物流配送模式优化","authors":"Yang Ming","doi":"10.1109/ICSGEA.2017.118","DOIUrl":null,"url":null,"abstract":"When vehicle path is performing express distribution for various branches in distribution center, we adopt simultaneous service of distribution strategy between express delivery and collection. It assumes that vehicles follow normal distribution of travel time among each point. Under condition that distribution branches have soft time window restriction and express delivery collection quantity follows Poisson distribution, the paper establish solution model of problems and performs genetic algorithm solution-based application design. GA algorithm is adopted to ensures the result effectiveness through fitness ranking and optimal individual-based selection strategy as well as parameter control of adaptive crossover probability. We also design numerical example with Matlab for experiment to prove the feasibility of our scheme.","PeriodicalId":326442,"journal":{"name":"2017 International Conference on Smart Grid and Electrical Automation (ICSGEA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Express Logistics Distribution Model Optimization for E-Commerce Environment\",\"authors\":\"Yang Ming\",\"doi\":\"10.1109/ICSGEA.2017.118\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When vehicle path is performing express distribution for various branches in distribution center, we adopt simultaneous service of distribution strategy between express delivery and collection. It assumes that vehicles follow normal distribution of travel time among each point. Under condition that distribution branches have soft time window restriction and express delivery collection quantity follows Poisson distribution, the paper establish solution model of problems and performs genetic algorithm solution-based application design. GA algorithm is adopted to ensures the result effectiveness through fitness ranking and optimal individual-based selection strategy as well as parameter control of adaptive crossover probability. We also design numerical example with Matlab for experiment to prove the feasibility of our scheme.\",\"PeriodicalId\":326442,\"journal\":{\"name\":\"2017 International Conference on Smart Grid and Electrical Automation (ICSGEA)\",\"volume\":\"10 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Conference on Smart Grid and Electrical Automation (ICSGEA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSGEA.2017.118\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Smart Grid and Electrical Automation (ICSGEA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSGEA.2017.118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Express Logistics Distribution Model Optimization for E-Commerce Environment
When vehicle path is performing express distribution for various branches in distribution center, we adopt simultaneous service of distribution strategy between express delivery and collection. It assumes that vehicles follow normal distribution of travel time among each point. Under condition that distribution branches have soft time window restriction and express delivery collection quantity follows Poisson distribution, the paper establish solution model of problems and performs genetic algorithm solution-based application design. GA algorithm is adopted to ensures the result effectiveness through fitness ranking and optimal individual-based selection strategy as well as parameter control of adaptive crossover probability. We also design numerical example with Matlab for experiment to prove the feasibility of our scheme.