Online meal delivery is undergoing explosive growth, as this service is becoming increasingly popular. A meal delivery platform aims to provide excellent and stable 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 crowd soucing drivers. The cancellation of the orders is incredibly detrimental to the customer's repurchase rate and the reputation of the Meituan meal delivery platform. To solve this problem, a certain amount of specific funds is provided by Meituan's business managers to encourage the crowdsourcing drivers to accept more orders. To make better use of the funds, in this work, we propose a framework to deal with the multi-stage 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 allocation algorithm. The semi-black-box acceptance probability model is employed to forecast the relationship between the bonus allocated to 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 allocation 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 25\% in reality.
翻译:在线餐饮供应正在发生爆炸性增长,因为这一服务越来越受欢迎。 餐饮供应平台旨在为顾客和餐馆提供极好和稳定的服务。 然而,在现实中,Meituan餐食供应平台每天取消几十万份订单,因为这些订单不被众推车司机所接受。 取消订单对客户的回购率和Meituan餐供应平台的声誉极为不利。 为了解决这个问题, Meituan的商业经理提供了一定数量的专项资金,鼓励众包驱动者接受更多的订单。 为了更好地利用资金,我们在此工作中提议了一个框架,以处理餐饮供应平台多阶段分红分配问题。 这个框架的目标是在有限的奖金预算范围内最大限度地增加被接受的订单数量。 这个框架包括半黑箱接受率模型、一个基于双基的动态程序制定算法,以及一个在线分配算法。 半黑箱框架的接受率模型用于预测为餐饮订单分配而分配的红利与接受率总概率之间的关系, 使用拉格朗基的每份双级分配算法, 一个基于双级算法, 一个基于双级的货币分配算法, 一个基于在线数据算算算算法, 一个基于系统, 一个基于双级的机算法, 一个基于双轨算算算法, 一个基于系统, 一个基于系统, 一个基于系统的递减价计算, 一个基于系统, 一个基于系统, 一个基于系统, 一个基于系统, 一个基于系统, 一个基于系统, 一个基于系统, 一个基于系统, 一个系统, 一个系统, 一个基于系统, 一个基于系统, 一个基于系统, 一个基于系统, 一个基于系统算算算算算算算算法, 一个基于系统, 一个基于系统, 一个基于系统计算, 一个基于系统算法, 一个系统算算法, 一个基于系统算算算算法, 一个基于系统, 一个基于系统算算算算算法, 一个系统算法, 一个系统算法, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统算, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统, 一个系统