{"type":"ScholarlyArticle","title":"CRSRL: Customer Routing System Using Reinforcement Learning","authors":[{"name":"Chong Long","slug":"chong-long","given":"Chong","family":"Long","affiliation":"Ant Financial Services Group , Z Space No. 556 Xixi Road Hangzhou , 310099 , Zhejiang , China"},{"name":"Zining Liu","slug":"zining-liu","given":"Zining","family":"Liu","affiliation":"School of Software , Shandong University , Jinan 250101 , Shandong , China"},{"name":"Xiaolu Lu","slug":"xiaolu-lu","given":"Xiaolu","family":"Lu","affiliation":"RMIT University , GPO Box 2476 , Melbourne VIC 3001 , Australia"},{"name":"Zehong Hu","slug":"zehong-hu","given":"Zehong","family":"Hu","affiliation":"Alibaba Group , 969 West Wen Yi Road , Hangzhou 311121 , Zhejiang , China"},{"name":"Yafang Wang","slug":"yafang-wang","given":"Yafang","family":"Wang","affiliation":"Ant Financial Services Group , Z Space No. 556 Xixi Road Hangzhou , 310099 , Zhejiang , China"}],"abstract":"Allocating resources to customers in the customer service is a difficult problem, because designing an optimal strategy to achieve an optimal trade-off between available resources and customers' satisfaction is non-trivial.\r\nIn this paper, we formalize the customer routing problem, and propose a novel framework based on deep reinforcement learning (RL) to address this problem. To make it more practical, a demo is provided to show and compare different models, which visualizes all decision process, and in particular, the system shows how the optimal strategy is reached. Besides, our demo system also ships with a variety of models that users can choose based on their needs.","doi":"10.24963/ijcai.2019/952","url":"https://ijcai.org/proceedings/2019/952","pdf":"https://ijcai.org/proceedings/2019/0952.pdf","markdown":"https://ijcai.org/proceedings/2019/952.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/952","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Demos","track":"Demos","number":952,"pages":{"first":6548,"last":6550},"keywords":["AI: Planning and Scheduling","AI: AI Modelling and Simulation"],"videos":[],"references":[{"position":1,"raw":"[Hosu and Rebedea, 2016] Ionel-Alexandru Hosu and Tra- ian Rebedea. 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ICML","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}