详细信息

Intelligent logistics integration of internal and external transportation with separation mode  ( SCI-EXPANDED收录 EI收录)  

文献类型:期刊文献

英文题名:Intelligent logistics integration of internal and external transportation with separation mode

作者:Fan, Tijun[1];Pan, Qianlan[1];Pan, Fei[1];Zhou, Wei[2,3];Chen, Jingyi[1]

机构:[1]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[2]ESCP Europe, Informat & Operat Management, F-75011 Paris, France;[3]Shanghai Jiaotung Univ, Sch Naval Architecture Ocean & Civil Engn, Transportat Engn, Shanghai 200240, Peoples R China

年份:2020

卷号:133

外文期刊名:TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW

收录:;EI(收录号:20233214523689);WOS:【SSCI(收录号:WOS:000513298400019),SCI-EXPANDED(收录号:WOS:000513298400019)】;

基金:This work was supported by the National Natural Science Foundation of China (71431004, 71972071) and the Fundamental Research Funds for the Central Universities.

语种:英文

外文关键词:Intelligent logistics; Container transportation; Integration; Separation mode

摘要:fMany multinational companies operate their business in both domestic and overseas markets with different logistics modes, namely freight transportation by truck for domestic and shorthaulage transshipment by container for overseas. To effectively optimize these operations for the two types of logistics systems, we propose an intelligent integration of external and internal transportation with the separation of drayage trucks and containers. The objective is to minimize the total cost, which includes both fixed and variable costs. The fixed cost occurs when the drayage truck is incurred in integrating transportation, and the variable cost is generated per travel distance increment. By dividing customers into different subsets and proposing a special penalty matrix, we provide an intelligent model that integrates the internal and external container transportation problems. A customized genetic algorithm is proposed. Based on the instances of real -life data on 888 orders, the results show that our approach can reduce the overall cost by 16.8%.

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