论文标题
餐点交付平台中多阶段奖金分配的框架
A Framework for Multi-stage Bonus Allocation in meal delivery Platform
论文作者
论文摘要
随着这项服务越来越流行，在线餐点正在爆炸性增长。餐点交付平台旨在为客户和餐馆提供出色而稳定的服务。但是，实际上，由于人群库司机不接受，每天在梅图恩餐点的平台中每天取消数十万个订单。取消订单对客户的回购率和Meituan用餐平台的声誉非常有害。为了解决这个问题，Meituan的业务经理提供了一定数量的特定资金，以鼓励众包驾驶员接受更多订单。为了更好地利用资金，在这项工作中，我们提出了一个框架，以处理餐点交付平台的多阶段奖金分配问题。该框架的目的是在有限的奖金预算中最大化公认的订单数量。该框架包括一个半布料盒的接受概率模型，一种基于拉格朗日双重双重的动态编程算法和在线分配算法。 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.为了验证我们框架的有效性和效率，在Meituan餐点交付平台上进行了现实世界数据集和在线A/B测试的离线实验。我们的结果表明，使用拟议的框架，总订单取消可以减少25 \％。
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.
