Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, Shopify, UberEats, InstaCart, and DoorDash are rapidly growing and are sharing the same business model of consumer items or food delivery. Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We observe a significant scope for reducing the costs associated with completing deliveries under the current model. We model our food delivery problem as a multi-objective optimization, where consumer satisfaction and delivery costs, both, need to be optimized. Taking inspiration from the success of ride-sharing in the taxi industry, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Unlike previous attempts for path-sharing, DeliverAI can provide real-time, time-efficient decision-making using a Reinforcement learning-enabled agent system. Our novel agent interaction scheme leverages path-sharing among deliveries to reduce the total distance traveled while keeping the delivery completion time under check. We generate and test our methodology vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 12\%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines.

翻译：过去十年来，从生产者到消费者的物品配送经历了显著增长，而近年来的疫情更是极大地推动了这一趋势的发展。Amazon Fresh、Shopify、UberEats、InstaCart 和 DoorDash 正在快速扩张，并且共享着消费品或食品配送的相同商业模式。现有的食品配送方法并非最优，因为每次配送都是独立优化的，旨在从生产者直接通过最短时间路径送达消费者。我们观察到，在当前模型下完成配送的成本有显著的降低空间。我们将食品配送问题建模为一个多目标优化问题，其中消费者满意度和配送成本都需要优化。受出租车行业拼车成功经验的启发，我们提出了 DeliverAI——一种基于强化学习的路径共享算法。与以往路径共享的尝试不同，DeliverAI 能够通过强化学习驱动的智能体系统提供实时且时间高效的决策。我们新颖的智能体交互方案利用配送间的路径共享来减少总行驶距离，同时保持配送完成时间在可控范围内。我们在基于芝加哥市真实数据的模拟设置上进行了严格的测试和验证。结果表明，与基线方法相比，DeliverAI 能够将配送车队规模减少 12%，行驶距离减少 13%，并实现车队利用率提升 50%。