CrowDNet: Enabling a Crowdsourced Object Delivery Network Based on Modern Portfolio Theory

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Abstract
In recent years, Takeout Ordering & Delivery (TOD) has become an emerging service due to its convenience and efficiency. However, current online ordering platforms still suffer from some issues, such as limited delivery coverage and delayed delivery. To address these issues, we propose a spatial crowdsourcing (SC)-based system called CrowDNet (Crowd Delivery Network) to have packages take hitchhiking rides with existing taxis. We first tackle passenger riding queries based on an evolutionary algorithm and then insert appropriate food delivery requests into a partial schedule with an improved insertion approach. Finally, we propose a ranking module based on the modern portfolio theory to recommend the delivery path, which can achieve a balance between the delivery cost and timely services. Evaluations based on three real-world datasets demonstrate that our proposed algorithms outperform baseline methods.
Funding Information
  • National Basic Research Program of China (2017YFB1001800)
  • National Natural Science Foundation of China (61772428, 61725205, 61602067)

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