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• W3194554490 abstract "Delivery stations play important roles in logistics systems. Well-designed delivery station planning can improve delivery efficiency significantly. However, existing delivery station locations are decided by experts, which requires much preliminary research and data collection work. It is not only time consuming but also expensive for logistics companies. Therefore, in this article, we propose a data-driven pipeline that can transfer expert knowledge among cities and automatically allocate delivery stations. Based on existing well-designed station location planning in the source city, we first train a model to learn the expert knowledge about delivery range selection for each station. Then we transfer the learned knowledge to a new city and design three strategies to select delivery stations for the new city. Due to the differences in characteristics among different cities, we adopt a transfer learning method to eliminate the domain difference so that the model can be adapted to a new city well. Finally, we conduct extensive experiments based on real-world datasets and find the proposed method can solve the problem well." @default.
• W3194554490 created "2021-08-30" @default.
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• W3194554490 date "2021-08-12" @default.
• W3194554490 modified "2023-09-28" @default.
• W3194554490 title "TLDS: A Transfer-Learning-Based Delivery Station Location Selection Pipeline" @default.
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• W3194554490 doi "https://doi.org/10.1145/3469084" @default.
• W3194554490 hasPublicationYear "2021" @default.
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