Robust Workforce Management with Crowdsourced Delivery
Chun Cheng, Melvyn Sim, Yue Zhao
Dalian University of Technology National University of Singapore Institute of Operations Research and Analytics
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摘要与影响
Revolutionize Workforce Management of Crowdsourced Delivery Platforms The surge in online shopping is driving e-retailers to revamp their logistics for efficient, cost-effective deliveries. Many are embracing crowdsourced delivery, where independent couriers use personal vehicles for swift shipments. Major players like Amazon and Walmart have pioneered this shift in delivery methods. To tackle uncertainties in this model, platforms are blending ad hoc couriers with prehired couriers. This hybrid approach ensures reliability in customer service while managing costs effectively. However, balancing this mix poses challenges, as future demands are still being determined. This study proposes a robust satisficing framework to optimize workforce management in delivery platforms. This innovative method aims to enhance cost-effectiveness and service quality by addressing uncertainties in ad hoc couriers’ availability and behavior. It offers a strategic tool for platforms to navigate workforce resources efficiently amidst fluctuating demands and cost constraints.
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工程Urban and Freight Transport Logistics
Vehicle Routing Optimization Methods · Digital Economy and Work Transformation
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