Stochastic scheduling and routing decisions in online meal delivery platforms with mixed force
研究了在线餐饮配送中随机调度与路径问题，提出基于马尔可夫决策过程的策略和四种集成方法，并用真实数据验证有效性，帮助管理者优化实时资源分配。
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 developping 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. • We study a meal delivery problem with crowdshipping. • Both requests and drivers’ availability arrive online. • We propose an MDP formulation. • We propose different heuristic algorithms. • We perform extensive computational tests.