An Analytical Model for Crowdsourcing Logistics Pricing Strategy Considering Demand Uncertainty

Crowdsourcing as an emerging logistics delivery model has lately received great attention due to its high integration rate of social resources and low costs of logistics distribution. Currently, crowdsourcing logistics pricing strategy considering the demand uncertainty is one of the main problems facing crowdsourcing. In this paper, the authors developed a crowdsourcing logistics pricing strategy model to handle the problem. The proposed model considers the factors affecting demand such as service level, price preference, rate of return, and delivery time. Based on the model, the influence of each factor on the optimal price of crowdsourcing logistics and the demand uncertainty brought by the crowdsourcing pricing strategy is investigated. Finally, the feasibility of the proposed crowdsourcing pricing strategy is verified and analyzed through numerical analysis. The results show that (1) a price reduction strategy should be adopted when consumers are more sensitive to service levels; (2) the courier can deliver more goods to the same location at one time, and when consumers have a high degree of preference for service prices, a price increase strategy should be used; and (3) when delivery possesses a higher preference for return rate, the rate of return or pricing should be increased, and when delivery possesses a higher preference for the expected delivery time, it is reasonable to advocate the use of price reduction strategies. Our method can provide theoretical and methodological insights for the study of crowdsourcing logistics pricing strategy.

• Record URL:
  • https://doi.org/10.1061/9780784485040.034
• Record URL:
  • https://ascelibrary.org/doi/10.1061/9780784485040.034
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/isbn/9780784485040
• Supplemental Notes:
  • © 2023 American Society of Civil Engineers.
• Corporate Authors:

  American Society of Civil Engineers

  1801 Alexander Bell Drive
  Reston, VA  United States  20191-4400
• Authors:
  • Zhou, Yutong
  • Zhao, Mingming
  • Miao, Hongzhi
  • Lin, Yu
• Conference:
  • 23rd COTA International Conference of Transportation Professionals
  • Location: Beijing , China
  • Date: 2023-7-14 to 2023-7-17
• Publication Date: 2023

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 340-350
• Monograph Title: CICTP 2023: Emerging Data-Driven Sustainable Technological Innovation in Transportation

Subject/Index Terms

• TRT Terms: Crowdsourcing; Demand; Logistics; Mathematical models; Pricing; Uncertainty
• Subject Areas: Finance; Freight Transportation; Planning and Forecasting;

Filing Info

• Accession Number: 01910218
• Record Type: Publication
• ISBN: 9780784485040
• Files: TRIS, ASCE
• Created Date: Feb 27 2024 4:40PM