Online meal delivery is undergoing explosive growth, as this service is becoming increasingly fashionable. A meal delivery platform aims to provide efficient services for customers and restaurants. However, in reality, several hundred thousand orders are canceled per day in the Meituan meal delivery platform since they are not accepted by the crowdsoucing drivers, which is detrimental to the interests of multiple stakeholders: customers, crowdsourcing drivers, restaurants, and the meal delivery platform. Therefore, allocating bonus is an effective means to encourage crowdsourcing drivers to accept more orders. In this study, we propose a framework to deal with the multistage bonus allocation problem for a meal delivery platform. The objective of this framework is to maximize the number of accepted orders within a limited bonus budget. This framework consists of a semi-black-box acceptance probability model, a Lagrangian dual-based dynamic programming algorithm, and an online algorithm. The semi-black-box acceptance probability model is employed to forecast the relationship between the bonus allocated to an order and its acceptance probability, the Lagrangian dual-based dynamic programming algorithm aims to calculate the empirical Lagrangian multiplier for each allocation stage offline based on the historical data set, and the online algorithm uses the results attained in the offline part to calculate a proper delivery bonus for each order. To verify the effectiveness and efficiency of our framework, both offline experiments on a real-world data set and online A/B tests on the Meituan meal delivery platform are conducted. Our results show that using the proposed framework, the total order cancellations can be decreased by more than 30% in reality.
Zhuolin Wu*, Li Wang*, Fangsheng Huang, et al. 2021. A Framework for Multistage Bonus Allocation in meal delivery Platform
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