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An optimization-driven dynamic vehicle routing algorithm for on-demand meal delivery using drones

机译:一种优化驱动的无人驾驶飞机按需送餐动态车辆路线算法

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摘要

As technology continues to improve people's quality of life, there is a large, unfulfilled market worldwide for on-demand meal delivery services. The competitive edge of the business is foremost sharpened by the agility of the transport system. While lightweight drones are being developed as the next-generation vehicular platform for meal delivery, an efficient fleet operation becomes especially critical. This paper presents a mixed integer programming (MIP) model that comprehensively characterizes all relevant aspects of the business scenario, and proposes an optimization-driven, progressive algorithm for online fleet dispatch operations. Different from typical graph-based formulations of vehicle routing problems, the proposed temporally discrete and spatially continuous MIP formulation endogenously accounts for geometry and mobility and therefore permits dynamic input of order information with arbitrary pickup and delivery locations. The model is augmented with special constraints and an artificial objective function which effectively relay the system states between successive time horizons. The algorithm is validated through simulation case studies and is shown to meet the design objectives. (C) 2019 Elsevier Ltd. All rights reserved.
机译:随着技术不断改善人们的生活质量,按需送餐服务在世界范围内有一个巨大的,尚未实现的市场。运输系统的敏捷性使企业的竞争优势更加明显。在开发轻型无人机作为送餐的下一代车载平台时,有效的机队运营变得尤为关键。本文提出了一种混合整数规划(MIP)模型,该模型全面地描述了业务场景的所有相关方面,并提出了一种用于在线机队调度操作的优化驱动的渐进算法。与典型的基于图形的车辆路径问题公式不同,建议的时间离散和空间连续的MIP公式内在地考虑了几何形状和移动性,因此允许在任意取件和交货位置动态输入订单信息。该模型增加了特殊约束和人工目标函数,可以有效地在连续时间范围之间传递系统状态。通过仿真案例研究验证了该算法,并证明该算法符合设计目标。 (C)2019 Elsevier Ltd.保留所有权利。

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