{"title":"4PL collaborative routing customization problem on the dynamic networks","authors":"Yan Cui, Min Huang, Qin Dai","doi":"10.1109/COASE.2017.8256289","DOIUrl":null,"url":null,"abstract":"Aiming at the dynamic time changes in the logistics transportation network, the problem that a Fourth Party Logistics (4PL) supplier collaborates Third Party Logistics (3PL) suppliers to customize routes for the customers is proposed. A mathematic programming model is presented by considering the transportation, staying and transit cost with the time constraint, and the dynamic time is updated at the transit nodes. A Two-phase solution method bases on the Ant Colony Optimization (TACO) is established. In the TACO, two equations of the state are given respectively for generating the best solution and renewing the ants' pheromone distribution. The numerical analysis shows that comparing with the algorithm repeated using the ACO, TACO cannot only save the running time, but also can increase the probability of finishing the task.","PeriodicalId":445441,"journal":{"name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COASE.2017.8256289","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Aiming at the dynamic time changes in the logistics transportation network, the problem that a Fourth Party Logistics (4PL) supplier collaborates Third Party Logistics (3PL) suppliers to customize routes for the customers is proposed. A mathematic programming model is presented by considering the transportation, staying and transit cost with the time constraint, and the dynamic time is updated at the transit nodes. A Two-phase solution method bases on the Ant Colony Optimization (TACO) is established. In the TACO, two equations of the state are given respectively for generating the best solution and renewing the ants' pheromone distribution. The numerical analysis shows that comparing with the algorithm repeated using the ACO, TACO cannot only save the running time, but also can increase the probability of finishing the task.