{"title":"Emergency Routing and Structural Optimization of E-commerce Logistics Network for Parcel Transportation Based on Multiple Models","authors":"","doi":"10.23977/jeis.2023.080308","DOIUrl":null,"url":null,"abstract":"The adjustment measures include closing or opening new routes, but not adding new logistics sites. To achieve dynamic adjustment of the logistics network's route structure, including the closure or development of new routes, the aim is to minimize the number of routes affected by changes in cargo volume before and after the closure of DC9, while maintaining a balanced workload among the routes. Therefore, an Ant Colony Optimization (ACO) algorithm model is established, and MATLAB and SPSSPRO are utilized to solve the prepared table based on the ACO model. The obtained routes DC69→DC5, DC69→DC8, DC69→DC14, and DC69→DC62 have a cargo conformity rate of 97%, with an average route workload of around 7%. The remaining cargo across all routes is 11,280.7. This indicates that the overall results remain unaffected after deleting DC9 and adding the new route DC3→DC1, with no routes exceeding the required conformity, satisfying the practical requirements. Next, an evaluation is conducted to assess the importance of different logistics sites and routes within the network. Taking into account basic conditions, such as parcel quantities, transport frequencies, maximum transport capacities, transfer capacities, and other influencing factors, a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis model is constructed. The processed table is used to analyze the network's robustness, determining appropriate settings for processing and transport capacities. The objective is to reduce the overall operating costs of the network while ensuring a more balanced distribution of network workload.","PeriodicalId":32534,"journal":{"name":"Journal of Electronics and Information Science","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electronics and Information Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23977/jeis.2023.080308","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The adjustment measures include closing or opening new routes, but not adding new logistics sites. To achieve dynamic adjustment of the logistics network's route structure, including the closure or development of new routes, the aim is to minimize the number of routes affected by changes in cargo volume before and after the closure of DC9, while maintaining a balanced workload among the routes. Therefore, an Ant Colony Optimization (ACO) algorithm model is established, and MATLAB and SPSSPRO are utilized to solve the prepared table based on the ACO model. The obtained routes DC69→DC5, DC69→DC8, DC69→DC14, and DC69→DC62 have a cargo conformity rate of 97%, with an average route workload of around 7%. The remaining cargo across all routes is 11,280.7. This indicates that the overall results remain unaffected after deleting DC9 and adding the new route DC3→DC1, with no routes exceeding the required conformity, satisfying the practical requirements. Next, an evaluation is conducted to assess the importance of different logistics sites and routes within the network. Taking into account basic conditions, such as parcel quantities, transport frequencies, maximum transport capacities, transfer capacities, and other influencing factors, a TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis model is constructed. The processed table is used to analyze the network's robustness, determining appropriate settings for processing and transport capacities. The objective is to reduce the overall operating costs of the network while ensuring a more balanced distribution of network workload.
调整措施包括关闭或开辟新的航线,但不增加新的物流站点。实现物流网络航线结构的动态调整,包括航线的关闭或新航线的开发,目的是尽量减少受DC9关闭前后货运量变化影响的航线数量,同时保持航线之间的工作量平衡。为此,建立了蚁群优化(Ant Colony Optimization, ACO)算法模型,并利用MATLAB和SPSSPRO对基于蚁群优化模型编制的表格进行求解。所得航线DC69→DC5、DC69→DC8、DC69→DC14、DC69→DC62的货物符合率为97%,平均航线负荷为7%左右。所有航线的剩余货物为11,280.7。说明删除DC9,添加新路由DC3→DC1后,整体结果没有影响,没有路由超过要求的符合性,满足实际需求。接下来,进行评估,以评估网络中不同物流地点和路线的重要性。考虑包裹数量、运输频率、最大运输能力、转运能力等基本条件影响因素,构建了TOPSIS (technical for Order of Preference by Similarity to Ideal Solution)分析模型。处理后的表用于分析网络的健壮性,确定处理和传输能力的适当设置。目标是降低网络的总体运营成本,同时确保更均衡地分配网络工作负载。