skip to main content
10.1145/3511808.3557132acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article
Public Access

Towards Fair Workload Assessment via Homogeneous Order Grouping in Last-mile Delivery

Published:17 October 2022Publication History

ABSTRACT

The popularity of e-commerce has promoted the rapid development of the logistics industry in recent years. As an important step in logistics, last-mile delivery from delivery stations to customers' addresses is now mainly finished by couriers, which requires accurate workload assessment based on actual efforts. However, the state-of-the-practice assessment methods neglect a vital factor that orders with the same customer's address (i.e., Homogeneous orders) can be delivered in a group (i.e., in a single trip) or separately (i.e., in multiple trips). It would cause unfair assessment among couriers if following the same rule. Thus, grouping homogeneous order accurately in the workload assessment is significant for achieving fair courier's workload assessment. To this end, we design, implement, and deploy a nationwide homogeneous order grouping system called FHOG for improving the accuracy of homogeneous order grouping in last-mile delivery for fair courier's workload assessment. FHOG utilizes the courier's reporting behavior for order inspection, collection, and delivery to identify homogeneous orders in the delivery station simultaneously for homogeneous order grouping. Compared with the state-of-the-practice method, our evaluation shows FHOG can effectively reduce order amounts with the higher and lower assessed courier's workload. We further deploy FHOG online in 8336 delivery stations to provide homogeneous order grouping service for more than 120 thousand couriers and 12 million daily orders. The results of the two surveys show that the couriers' acceptance rate is improved by 67% with FHOG after the promotion.

References

  1. Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286 (2019).Google ScholarGoogle Scholar
  2. Ali-akbar Agha-mohammadi, N Kemal Ure, Jonathan P How, and John Vian. 2014. Health aware stochastic planning for persistent package delivery missions using quadrotors. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 3389--3396.Google ScholarGoogle Scholar
  3. Robin Burke, Nasim Sonboli, and Aldo Ordonez-Gauger. 2018. Balanced neighborhoods for multi-sided fairness in recommendation. In Conference on Fairness, Accountability and Transparency. PMLR, 202--214.Google ScholarGoogle Scholar
  4. Chao Chen, Daqing Zhang, Xiaojuan Ma, Bin Guo, Leye Wang, Yasha Wang, and Edwin Sha. 2016. Crowddeliver: Planning city-wide package delivery paths leveraging the crowd of taxis. IEEE Transactions on Intelligent Transportation Systems, Vol. 18, 6 (2016), 1478--1496.Google ScholarGoogle Scholar
  5. Yueyue Chen, Deke Guo, Ming Xu, Guoming Tang, Tongqing Zhou, and Bangbang Ren. 2019. PPtaxi: Non-Stop Package Delivery via Multi-Hop Ridesharing. IEEE Transactions on Mobile Computing, Vol. 19, 11 (2019), 2684--2698.Google ScholarGoogle ScholarCross RefCross Ref
  6. Yue-Yue Chen, Pin Lv, De-Ke Guo, Tong-Qing Zhou, and Ming Xu. 2018. A survey on task and participant matching in mobile crowd sensing. Journal of Computer Science and Technology, Vol. 33, 4 (2018), 768--791.Google ScholarGoogle ScholarCross RefCross Ref
  7. Catherine Cleophas, Caitlin Cottrill, Jan Fabian Ehmke, and Kevin Tierney. 2019. Collaborative urban transportation: Recent advances in theory and practice. European Journal of Operational Research, Vol. 273, 3 (2019), 801--816.Google ScholarGoogle ScholarCross RefCross Ref
  8. Yi Ding, Baoshen Guo, Lin Zheng, Mingming Lu, Desheng Zhang, Shuai Wang, Sang Hyuk Son, and Tian He. 2021a. A City-Wide Crowdsourcing Delivery System with Reinforcement Learning. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 5, 3 (2021), 1--22.Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Yi Ding, Ling Liu, Yu Yang, Yunhuai Liu, Desheng Zhang, and Tian He. 2021b. From conception to retirement: a lifetime story of a 3-year-old wireless beacon system in the wild. IEEE/ACM Transactions on Networking (2021).Google ScholarGoogle Scholar
  10. Yi Ding, Yu Yang, Wenchao Jiang, Yunhuai Liu, Tian He, and Desheng Zhang. 2021c. Nationwide deployment and operation of a virtual arrival detection system in the wild. In Proceedings of the 2021 ACM SIGCOMM 2021 Conference. 705--717.Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Parisa Dolati Neghabadi, Karine Evrard Samuel, and Marie-Laure Espinouse. 2019. Systematic literature review on city logistics: overview, classification and analysis. International Journal of Production Research, Vol. 57, 3 (2019), 865--887.Google ScholarGoogle ScholarCross RefCross Ref
  12. Zuohui Fu, Yikun Xian, Ruoyuan Gao, Jieyu Zhao, Qiaoying Huang, Yingqiang Ge, Shuyuan Xu, Shijie Geng, Chirag Shah, Yongfeng Zhang, et al. 2020. Fairness-aware explainable recommendation over knowledge graphs. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 69--78.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Ruoyuan Gao and Chirag Shah. 2019. How fair can we go: Detecting the boundaries of fairness optimization in information retrieval. In Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval. 229--236.Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Yingqiang Ge, Shuchang Liu, Ruoyuan Gao, Yikun Xian, Yunqi Li, Xiangyu Zhao, Changhua Pei, Fei Sun, Junfeng Ge, Wenwu Ou, et al. 2021. Towards Long-term Fairness in Recommendation. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining. 445--453.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Wei Gong, Baoxian Zhang, and Cheng Li. 2018. Task assignment in mobile crowdsensing: Present and future directions. IEEE network, Vol. 32, 4 (2018), 100--107.Google ScholarGoogle Scholar
  16. Bin Guo, Yan Liu, Leye Wang, Victor OK Li, Jacqueline CK Lam, and Zhiwen Yu. 2018. Task allocation in spatial crowdsourcing: Current state and future directions. IEEE Internet of Things Journal, Vol. 5, 3 (2018), 1749--1764.Google ScholarGoogle ScholarCross RefCross Ref
  17. Baoshen Guo, Shuai Wang, Yi Ding, Guang Wang, Suining He, Desheng Zhang, and Tian He. 2021. Concurrent Order Dispatch for Instant Delivery with Time-Constrained Actor-Critic Reinforcement Learning. In 2021 IEEE Real-Time Systems Symposium (RTSS). IEEE, 176--187.Google ScholarGoogle Scholar
  18. Daeki Kim, Cynthia Barnhart, Keith Ware, and Gregory Reinhardt. 1999. Multimodal express package delivery: A service network design application. Transportation Science, Vol. 33, 4 (1999), 391--407.Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Yexin Li, Yu Zheng, and Qiang Yang. 2019. Efficient and effective express via contextual cooperative reinforcement learning. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 510--519.Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Yexin Li, Yu Zheng, and Qiang Yang. 2020. Cooperative Multi-Agent Reinforcement Learning in Express System. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 805--814.Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Rishabh Mehrotra, James McInerney, Hugues Bouchard, Mounia Lalmas, and Fernando Diaz. 2018. Towards a fair marketplace: Counterfactual evaluation of the trade-off between relevance, fairness & satisfaction in recommendation systems. In Proceedings of the 27th acm international conference on information and knowledge management. 2243--2251.Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Marco Morik, Ashudeep Singh, Jessica Hong, and Thorsten Joachims. 2020. Controlling fairness and bias in dynamic learning-to-rank. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 429--438.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Yan Pan, Shining Li, Qianwu Chen, Nan Zhang, Tao Cheng, Zhigang Li, Bin Guo, Qingye Han, and Ting Zhu. 2021. Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 5, 1 (2021), 1--23.Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Sijie Ruan, Zi Xiong, Cheng Long, Yiheng Chen, Jie Bao, Tianfu He, Ruiyuan Li, Shengnan Wu, Zhongyuan Jiang, and Yu Zheng. 2020. Doing in One Go: Delivery Time Inference Based on Couriers' Trajectories. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2813--2821.Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Adam Sadilek, John Krumm, and Eric Horvitz. 2013. Crowdphysics: Planned and opportunistic crowdsourcing for physical tasks. In Seventh international AAAI conference on weblogs and social media.Google ScholarGoogle Scholar
  26. Dingyuan Shi, Yongxin Tong, Zimu Zhou, Bingchen Song, Weifeng Lv, and Qiang Yang. 2021. Learning to assign: Towards fair task assignment in large-scale ride hailing. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 3549--3557.Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Hien To, Gabriel Ghinita, Liyue Fan, and Cyrus Shahabi. 2016. Differentially private location protection for worker datasets in spatial crowdsourcing. IEEE Transactions on Mobile Computing, Vol. 16, 4 (2016), 934--949.Google ScholarGoogle Scholar
  28. Fangxin Wang, Yifei Zhu, Feng Wang, and Jiangchuan Liu. 2018. Ridesharing as a service: Exploring crowdsourced connected vehicle information for intelligent package delivery. In 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS). IEEE, 1--10.Google ScholarGoogle ScholarCross RefCross Ref
  29. Guang Wang, Yongfeng Zhang, Zhihan Fang, Shuai Wang, Fan Zhang, and Desheng Zhang. 2020. FairCharge: A data-driven fairness-aware charging recommendation system for large-scale electric taxi fleets. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 4, 1 (2020), 1--25.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Guang Wang, Shuxin Zhong, Shuai Wang, Fei Miao, Zheng Dong, and Desheng Zhang. 2021. Data-Driven Fairness-Aware Vehicle Displacement for Large-Scale Electric Taxi Fleets. In 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 1200--1211.Google ScholarGoogle Scholar
  31. Zaiyue Yang, Tianci Guo, Pengcheng You, Yunhe Hou, and S Joe Qin. 2018. Distributed approach for temporal--spatial charging coordination of plug-in electric taxi fleet. IEEE Transactions on Industrial Informatics, Vol. 15, 6 (2018), 3185--3195.Google ScholarGoogle ScholarCross RefCross Ref
  32. Sirui Yao and Bert Huang. 2017. Beyond parity: Fairness objectives for collaborative filtering. arXiv preprint arXiv:1705.08804 (2017).Google ScholarGoogle Scholar
  33. Yuxiang Zeng, Yongxin Tong, and Lei Chen. 2019. Last-mile delivery made practical: An efficient route planning framework with theoretical guarantees. Proceedings of the VLDB Endowment, Vol. 13, 3 (2019), 320--333.Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Siyuan Zhang, Lu Qin, Yu Zheng, and Hong Cheng. 2016. Effective and efficient: Large-scale dynamic city express. IEEE Transactions on Knowledge and Data Engineering, Vol. 28, 12 (2016), 3203--3217.Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Yu Zheng. 2015. Trajectory data mining: an overview. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 6, 3 (2015), 1--41.Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Towards Fair Workload Assessment via Homogeneous Order Grouping in Last-mile Delivery

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
        October 2022
        5274 pages
        ISBN:9781450392365
        DOI:10.1145/3511808
        • General Chairs:
        • Mohammad Al Hasan,
        • Li Xiong

        Copyright © 2022 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 17 October 2022

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        CIKM '22 Paper Acceptance Rate621of2,257submissions,28%Overall Acceptance Rate1,861of8,427submissions,22%

        Upcoming Conference

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader