Two-Stage Dynamic Optimization on Station-to-Door Delivery with Uncertain Freight Operation Time in Urban Logistics
Publication: Journal of Urban Planning and Development
Volume 148, Issue 3
Abstract
In the field of modern urban logistics, the development of door-to-door freight transport through rail–road combined transportation is a necessary approach to achieve modernization and smartness of railroad freight transportation. As the last step of door-to-door rail–road joint transportation, station-to-door transportation determines the quality and efficiency of services. Meanwhile, the operation time of goods assembly is uncertain in the freight center station, freight handling station, and in transit, which largely limits the efficiency of rail–road combined transportation delivery at the stage of station-to-door. To address the aforementioned problems, we proposed a forward-looking matching strategy (FL) that jointly considers the set of goods orders that can be fulfilled in the current decision stage and the set of goods orders that can only be fulfilled in the future stage to improve the matching effect. Then, we built a two-stage stochastic dynamic programming model that jointly considers matching between goods orders and distribution path optimization. At the same time, we simplified the complex model by using a Bayesian approach to update the goods’ operation time in real time. Finally, we designed an improved differential evolution algorithm based on order similarity and distribution for solving the optimization. The algorithm we designed reduces 34.69% in transportation cost and 31.37% in waiting time cost compared with the actual delivery plan implemented.
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Acknowledgments
This work was supported by grants from the National Natural Science Foundation of China (Project Nos. 716011163, 61703351, 71671100, 52072314, 52172321, and 52102391), China Shenhua Energy Science and Technology Program (Project No. CJNY-20-02), China National Railway Group Co., Science and Technology Research Program Project (Project No. P2020X016, 2019F002), the General Project of Key R&D Program of Jiangxi Province (Grant Nos. 20192BBG70076 and 20203BBG73072), and key research-based for the National Engineering Laboratory of Integrated Transportation Big Data Application Technology, China. Zhiyuan Li designed a model and algorithm and proposed research ideas. Chenhao Wang was responsible for data analysis. Ni Dong wrote the first draft of the study. Minghua Zeng, Pengpeng Xu, and Zongying Song revised the study and corrected the algorithm defects.
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Published In
Journal of Urban Planning and Development
Volume 148 • Issue 3 • September 2022
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Oct 31, 2021
Accepted: Mar 1, 2022
Published online: Apr 20, 2022
Published in print: Sep 1, 2022
Discussion open until: Sep 20, 2022
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