论文标题
智能在线食品交付系统：一种动态模型，以生成交付策略和提示建议
Intelligent Online Food Delivery System: A Dynamic Model to Generate Delivery Strategy and Tip Advice
论文作者
论文摘要
由于在线食品订购平台的快速发展和需求的增长，市场即将饱和，未来的趋势是寻求有效利用资源。具体而言，食品公司必须具有可靠的算法来帮助他们制定有效的交付策略；各个客户需要计划在这一领域的决策。例如，当客户以控制或减少延迟而为订单添加提示。但是，很少有客户知道有多少技巧足以达到所需的延迟。因此，在我们的论文中，我们建立了一个动态模型，以为公司生成交付策略并为客户提供建议。我们认为，我们设计的系统比当前原始系统更有效。我们使用遗传退火来模拟输送过程并生成交付策略，因为它可以接近最佳的解决方案。高质量的交付队列可确保可以在可接受的时间内交付这些订单。接下来，我们构建回归，以找出多个因素与延迟之间的关系，然后生成小费的咨询量。最后，我们插入了这些值，并希望等待时间获得咨询提示价格。多个索引表明我们的回归结果是准确且可靠的。
Due to the rapid development of online food ordering platforms and rocketing growth of demand, the market is about to saturate soon, and the future trend is to seek efficient utilization of resources. Specifically speaking, food company must have a reliable algorithm to help them produce efficient delivery strategies; individual customers need planning for their decision making in this field. For example, when customers add tip to their order with the sake of controlling or reducing latency. However, few customers know how much tip is enough to reach their desired latency. Therefore, in our paper, we establish a dynamic model to generate delivery strategy for companies and tip advice for customers. We believe that the system we design is more efficient than the currently primitive system. We simulate the delivery process and generate delivery strategies using genetic annealing because it can approach a near optimal solution. High-quality delivery queue ensures that those orders can be delivered within an acceptable amount of time. Next, we construct regressions to find out relationships between multiple factors and latency and then generate the advisory amount of tip. Finally, we plug in those values and desired waiting time, getting the advisory tip price. Multiple indexes suggest that our regression results are accurate and reliable.
