最后一英里基于热点的新型食品配送分布式路径共享网络

IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL
Ashman Mehra;Divyanshu Singh;Vaskar Raychoudhury;Archana Mathur;Snehanshu Saha
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引用次数: 0

摘要

在过去十年中,从生产者到消费者的物品配送经历了显著增长,最近的大流行病更是大大推动了这一增长。Amazon Fresh、GrubHub、UberEats、Postmates、InstaCart 和 DoorDash 都在迅速发展,并共享相同的消费物品或食品配送业务模式。现有的食品配送方式都是次优的,因为每种配送方式都是经过单独优化的,通过最短的时间路径直接从生产者送到消费者手中。我们发现,在当前的模式下,与完成送货相关的成本还有很大的降低空间。为此,我们将食品配送问题建模为多目标优化,即消费者满意度和配送成本都需要优化。从出租车行业共享乘车的成功经验中汲取灵感,我们提出了基于强化学习的路径共享算法--DeliverAI。与以往的路径共享尝试不同,DeliverAI 可以利用强化学习代理系统提供实时、省时的决策。我们新颖的代理交互方案利用交货之间的路径共享来减少总路程,同时控制交货完成时间。我们利用芝加哥市的真实数据在模拟设置中生成并测试了我们的方法。我们的结果表明,DeliverAI 可以将送货车队的规模缩小 15%,行驶距离缩短 16%,车队利用率比点对点送货系统高出 50%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Last Mile: A Novel, Hotspot-Based Distributed Path-Sharing Network for Food Deliveries
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, GrubHub, UberEats, Postmates, 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. For this, 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 15%, the distance traveled by 16%, and 50% higher fleet utilization w.r.t point-to-point delivery systems.
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来源期刊
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems 工程技术-工程:电子与电气
CiteScore
14.80
自引率
12.90%
发文量
1872
审稿时长
7.5 months
期刊介绍: The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.
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