{"type":"ScholarlyArticle","title":"A Prediction-and-Scheduling Framework for Efficient Order Transfer in Logistics","authors":[{"name":"Wenjun Lyu","slug":"wenjun-lyu","given":"Wenjun","family":"Lyu","affiliation":"Rutgers University ,"},{"name":"Haotian Wang","slug":"haotian-wang","given":"Haotian","family":"Wang","affiliation":""},{"name":"Yiwei Song","slug":"yiwei-song","given":"Yiwei","family":"Song","affiliation":""},{"name":"Yunhuai Liu","slug":"yunhuai-liu","given":"Yunhuai","family":"Liu","affiliation":"Peking University"},{"name":"Tian He","slug":"tian-he","given":"Tian","family":"He","affiliation":""},{"name":"Desheng Zhang","slug":"desheng-zhang","given":"Desheng","family":"Zhang","affiliation":"Rutgers University ,"}],"abstract":"Order Transfer from the transfer center to delivery stations is an essential and expensive part of the logistics service chain. In practice, one vehicle sends transferred orders to multiple delivery stations in one transfer trip to achieve a better trade-off between the transfer cost and time. A key problem is generating the vehicle’s route for efficient order transfer, i.e., minimizing the order transfer time. In this paper, we explore fine-grained delivery station features, i.e., downstream couriers’ remaining working times in last-mile delivery trips and the transferred order distribution to design a Prediction-and-Scheduling framework for efficient Order Transfer called PSOT, including two components: i) a Courier’s Remaining Working Time Prediction component to predict each courier’s working time for conducting heterogeneous tasks, i.e., order pickups and deliveries, with a context-aware location embedding and an attention-based neural network; ii) a Vehicle Scheduling component to generate the vehicle’s route to served delivery stations with an order-transfer-time-aware heuristic algorithm. The evaluation results with real-world data from one of the largest logistics companies in China show PSOT improves the courier’s remaining working time prediction by up to 35.6% and reduces the average order transfer time by up to 51.3% compared to the state-of-the-art methods.","doi":"10.24963/ijcai.2023/680","url":"https://ijcai.org/proceedings/2023/680","pdf":"https://ijcai.org/proceedings/2023/0680.pdf","markdown":"https://ijcai.org/proceedings/2023/680.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/680","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":680,"pages":{"first":6130,"last":6137},"keywords":["AI for Good: Data Mining","AI for Good: Humans and AI","AI for Good: Multidisciplinary Topics and Applications"],"videos":[],"references":[{"position":1,"raw":"[Bello et al., 2016] Irwan Bello, Hieu Pham, Quoc V Le, Mohammad Norouzi, and Samy Bengio. 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