Treffer: Robust Workforce Management with Crowdsourced Delivery.

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. We investigate how crowdsourced delivery platforms with both contracted and ad hoc couriers can effectively manage their workforce to meet delivery demands amidst uncertainties. Our objective is to minimize the hiring costs of contracted couriers and the crowdsourcing costs of ad hoc couriers, while considering the uncertain availability and behavior of the latter. Because of the complication of calibrating these uncertainties through data-driven approaches, we instead introduce a basic reduced information model to estimate the upper bound of the crowdsourcing cost and a generalized reduced information model to obtain a tighter bound. Subsequently, we formulate a robust satisficing model associated with the generalized reduced information model and show that a binary search algorithm can tackle the model exactly by solving a modest number of convex optimization problems. Our numerical tests using Solomon's data sets show that reduced information models provide decent approximations for practical delivery scenarios. Simulation tests further demonstrate that the robust satisficing model has better out-of-sample performance than the empirical optimization model that minimizes the total cost under historical scenarios. Funding: C. Cheng was supported by the National Natural Science Foundation of China [Grants 72471042, 72101049, and 72232001] and the Fundamental Research Funds for the Central Universities [Grant DUT23RC(3)045]. M. Sim and Y. Zhao were supported by the Ministry of Education, Singapore, under its 2019 Academic Research Fund Tier 3 [Grant MOE-2019-T3-1-010]. Supplemental Material: All supplemental materials, including the computer code and data that support the findings of this study, are available at https://doi.org/10.1287/opre.2023.0125. [ABSTRACT FROM AUTHOR]

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