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Carrier App: Leveraging Machine Learning for Secure Crowdshipping

  • Unique Paper ID: 167193
  • Volume: 7
  • Issue: 8
  • PageNo: 296-306
  • Abstract:
  • This paper explores a new approach to delivery using crowd-shipping. Unlike traditional company-owned delivery vehicles, crowd-shipping utilizes everyday people with personal vehicles to deliver packages for compensation. This system offers greater flexibility and cost savings. The focus here is on a dynamic system where in-store customers are recruited as crowd-shippers to deliver online orders on their way home. However, these customers can choose to accept or decline the delivery offer. The challenge is to find the best way to match online orders with available crowd-shippers and determine the optimal compensation for each delivery. To address this, the paper proposes a two-step optimization model. This model first determines the best matches between orders and crowd-shippers, then calculates the optimal compensation scheme. Through computer simulations, the model demonstrates a significant cost reduction, averaging 7.30% lower compared to traditional delivery methods.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 7
  • Issue: 8
  • PageNo: 296-306

Carrier App: Leveraging Machine Learning for Secure Crowdshipping

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8.01 (Year 2024)
UGC Approved
Journal no 47859

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