This paper investigates stochastic scheduling and routing problems in the online meal delivery (OMD) service. The huge increase in meal delivery demand requires the service providers to construct a highly efficient logistics network to deal with a large-volume of time-sensitive and fluctuating fulfillment, often using inhouse and crowdsourced drivers to secure the ambitious service quality. We aim to address the problem of developing an effective scheduling and routing policy that can handle real-life situations. To this end, we first model the dynamic problem as a Markov Decision Process (MDP) and analyze the structural properties of the optimal policy. Then we propose four integrated approaches to solve the operational level scheduling and routing problem. In addition, we provide a continuous approximation formula to estimate the bounds of required fleet size for the inhouse drivers. Numerical experiments based on a real dataset show the effectiveness of the proposed solution approaches. We also obtain several managerial insights that can help decision makers in solving similar resource allocation problems in real-time.

中文翻译：

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混合力量在线送餐平台中的随机调度和路线决策


本文研究了在线送餐 （OMD） 服务中的随机调度和路由问题。送餐需求的巨大增长要求服务提供商构建高效的物流网络，以应对大量时间敏感和波动的履行，通常使用内部和众包司机来确保雄心勃勃的服务质量。我们的目标是解决开发能够处理实际情况的有效调度和路由策略的问题。为此，我们首先将动态问题建模为马尔可夫决策过程 （MDP），并分析最优策略的结构特性。然后，我们提出了四种集成方法来解决运营级别的调度和路由问题。此外，我们还提供了一个连续近似公式来估计内部驾驶员所需的车队规模的界限。基于真实数据集的数值实验证明了所提出的解方法的有效性。我们还获得了一些管理见解，可以帮助决策者实时解决类似的资源分配问题。