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Synchronizing e-commerce city logistics with sliding time windows

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  • Shao, Saijun
  • Xu, Gangyan
  • Li, Ming
  • Huang, George Q.

Abstract

The cost-effective and high-quality city logistics service is a key for the success of e-commerce enterprises. Synchronization (sync) is emerging as a typical yet challenging requirement, which asks for simultaneous deliveries of multiple products to the same customer. This paper is among the first to model the sync constraints with sliding time windows (STW). STW is a special type of time window of which only the window size is defined. Unlike traditional time windows, the start and end time of STW could be adjusted earlier or later, so long as their difference equals to the pre-defined window size. In this sense, the STW is a time constraint with partially unknown factors. Such flexibility of STW can greatly improve the efficiency of vehicle tours, because customers could be served in a more flexible sequence decided by the transporter. A novel divide and conquer based algorithm is developed to tackle the proposed problem with partially unknown time constraints. The values for STWs will dynamically be determined during the algorithm. Numerical studies show that by modelling sync requirements with STW, transportation cost could be saved as much as in half. We also carry out sensitivity analyses on the key factors such as promised sync level to customers and the complexity of online orders.

Suggested Citation

  • Shao, Saijun & Xu, Gangyan & Li, Ming & Huang, George Q., 2019. "Synchronizing e-commerce city logistics with sliding time windows," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 17-28.
    • Handle: RePEc:eee:transe:v:123:y:2019:i:c:p:17-28
    DOI: 10.1016/j.tre.2019.01.007
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    Cited by:

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    4. Elpida Xenou & Michael Madas & Georgia Ayfandopoulou, 2022. "Developing a Smart City Logistics Assessment Framework (SCLAF): A Conceptual Tool for Identifying the Level of Smartness of a City Logistics System," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    5. Michał Lasota & Aleksandra Zabielska & Marianna Jacyna & Piotr Gołębiowski & Renata Żochowska & Mariusz Wasiak, 2024. "Method for Delivery Planning in Urban Areas with Environmental Aspects," Sustainability, MDPI, vol. 16(4), pages 1-18, February.
    6. Thais de Castro Moraes & Jiancheng Qin & Xue-Ming Yuan & Ek Peng Chew, 2023. "Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions," Logistics, MDPI, vol. 7(4), pages 1-18, November.
    7. Wang, Haibo & Alidaee, Bahram, 2023. "White-glove service delivery: A quantitative analysis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    8. Fernanda Alves de Araújo & João Gilberto Mendes dos Reis & Marcia Terra da Silva & Emel Aktas, 2022. "A Fuzzy Analytic Hierarchy Process Model to Evaluate Logistics Service Expectations and Delivery Methods in Last-Mile Delivery in Brazil," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    9. Jiacheng Xu & Jianjun Bai & Jun Chen, 2019. "An Improved Indicator System for Evaluating the Progress of Sustainable Development Goals (SDGs) Sub-Target 9.1 in County Level," Sustainability, MDPI, vol. 11(17), pages 1-22, September.
    10. Xin Yao & Yuanyuan Cheng & Li Zhou & Malin Song, 2022. "Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods," Annals of Operations Research, Springer, vol. 308(1), pages 727-752, January.

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