IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i18p7936-d1475975.html

Research on Express Crowdsourcing Task Allocation Considering Distribution Mode under Customer Classification

Author

Listed:
  • Xiaohu Xing

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Chang Sun

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

  • Xinqiang Chen

    (Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China)

Abstract

In order to promote the sustainable development of crowdsourcing logistics and control the cost of crowdsourcing logistics while improving the quality of crowdsourcing services, this paper proposes a courier crowdsourcing task allocation model that considers delivery methods under customer classification, with the optimization objective of minimizing the total cost of the crowdsourcing platform. This model adopts two delivery modes: home delivery by crowdsource couriers and pickup by customers. Customers can freely choose the express delivery method according to their actual situation when placing orders, thus better meeting their needs. Based on the customer’s historical express-consumption data, the entropy weight RFM model is used to classify them, and different penalty functions are constructed for different categories of customers to reduce the total delivery cost and improve the on-time delivery of efficient and potential customers. And a Customer Classification Genetic Algorithm (CCGA) was designed for simulation experiments, which showed that the algorithm proposed in this study significantly improved the local search ability, thereby optimizing the delivery task path of express crowdsourcing. This improvement not only improves the delivery timeliness for efficient and potential customers, but also effectively reduces the total delivery cost. Therefore, the research on parcel crowdsourcing task allocation based on customer classification reduces the cost of crowdsourcing delivery platforms and improves customer satisfaction, which has certain theoretical research value and practical-application significance.

Suggested Citation

  • Xiaohu Xing & Chang Sun & Xinqiang Chen, 2024. "Research on Express Crowdsourcing Task Allocation Considering Distribution Mode under Customer Classification," Sustainability, MDPI, vol. 16(18), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:7936-:d:1475975
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/18/7936/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/18/7936/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sekandari Elham & Aghaei Iman, 2023. "Exploring Must-know Criteria for Effective Customer Segmentation in Online Market Using AHP," Review of Marketing Science, De Gruyter, vol. 21(1), pages 271-294, September.
    2. Archetti, Claudia & Savelsbergh, Martin & Speranza, M. Grazia, 2016. "The Vehicle Routing Problem with Occasional Drivers," European Journal of Operational Research, Elsevier, vol. 254(2), pages 472-480.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Garcia-Herrera, Alisson & Serrano-Hernandez, Adrian & Faulin, Javier, 2025. "Understanding the dynamics of crowdshipping in last-mile distribution within urban mobility: A comprehensive framework," Socio-Economic Planning Sciences, Elsevier, vol. 101(C).
    2. Ermagun, Alireza & Stathopoulos, Amanda, 2018. "To bid or not to bid: An empirical study of the supply determinants of crowd-shipping," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 468-483.
    3. Chun Cheng & Melvyn Sim & Yue Zhao, 2025. "Robust Workforce Management with Crowdsourced Delivery," Operations Research, INFORMS, vol. 73(2), pages 595-612, March.
    4. Tao Yang & Weixin Wang, 2022. "Logistics Network Distribution Optimization Based on Vehicle Sharing," Sustainability, MDPI, vol. 14(4), pages 1-12, February.
    5. Lin Zhou & Yanping Chen & Yi Jing & Youwei Jiang, 2021. "Evolutionary Game Analysis on Last Mile Delivery Resource Integration—Exploring the Behavioral Strategies between Logistics Service Providers, Property Service Companies and Customers," Sustainability, MDPI, vol. 13(21), pages 1-18, November.
    6. Punel, Aymeric & Stathopoulos, Amanda, 2017. "Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 105(C), pages 18-38.
    7. Zhang, Jian & Woensel, Tom Van, 2023. "Dynamic vehicle routing with random requests: A literature review," International Journal of Production Economics, Elsevier, vol. 256(C).
    8. Yang Liu & Sen Li, 2025. "Piggyback on Idle Ride-Sourcing Drivers for Integrated On-Demand and Flexible Intracity Parcel Delivery Services," Transportation Science, INFORMS, vol. 59(3), pages 494-517, June.
    9. Mario Binetti & Leonardo Caggiani & Rosalia Camporeale & Michele Ottomanelli, 2019. "A Sustainable Crowdsourced Delivery System to Foster Free-Floating Bike-Sharing," Sustainability, MDPI, vol. 11(10), pages 1-24, May.
    10. Pourrahmani, Elham & Jaller, Miguel, 2021. "Crowdshipping in last mile deliveries: Operational challenges and research opportunities," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    11. Alnaggar, Aliaa & Gzara, Fatma & Bookbinder, James H., 2021. "Crowdsourced delivery: A review of platforms and academic literature," Omega, Elsevier, vol. 98(C).
    12. Ausseil, Rosemonde & Ulmer, Marlin W. & Pazour, Jennifer A., 2024. "Online acceptance probability approximation in peer-to-peer transportation," Omega, Elsevier, vol. 123(C).
    13. dos Santos, André Gustavo & Viana, Ana & Pedroso, João Pedro, 2022. "2-echelon lastmile delivery with lockers and occasional couriers," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    14. Yıldız, Barış, 2021. "Package routing problem with registered couriers and stochastic demand," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 147(C).
    15. Azcuy, Irecis & Agatz, Niels & Giesen, Ricardo, 2021. "Designing integrated urban delivery systems using public transport," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    16. Belma Turan & Vera Hemmelmayr & Allan Larsen & Jakob Puchinger, 2024. "Transition towards sustainable mobility: the role of transport optimization," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 32(2), pages 435-456, June.
    17. Rosemonde Ausseil & Jennifer A. Pazour & Marlin W. Ulmer, 2022. "Supplier Menus for Dynamic Matching in Peer-to-Peer Transportation Platforms," Transportation Science, INFORMS, vol. 56(5), pages 1304-1326, September.
    18. Martín-Santamaría, Raúl & López-Ibáñez, Manuel & Stützle, Thomas & Colmenar, J. Manuel, 2024. "On the automatic generation of metaheuristic algorithms for combinatorial optimization problems," European Journal of Operational Research, Elsevier, vol. 318(3), pages 740-751.
    19. Zhang, Zhuoye & Zhang, Fangni, 2024. "Optimal operation strategies of an urban crowdshipping platform in asset-light, asset-medium, or asset-heavy business format," Transportation Research Part B: Methodological, Elsevier, vol. 189(C).
    20. Simona Mancini & Margaretha Gansterer, 2024. "Bundle generation for the vehicle routing problem with occasional drivers and time windows," Flexible Services and Manufacturing Journal, Springer, vol. 36(4), pages 1189-1221, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:7936-:d:1475975. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.