Restaurant meal delivery has been rapidly growing in the last few years. The main challenges in operating it are the temporally and spatially dispersed stochastic demand that arrives from customers all over town as well as the customers' expectation of timely and fresh delivery. To overcome these challenges a new business concept emerged, "Ghost kitchens". This concept proposes synchronized food preparation of several restaurants in a central complex, exploiting consolidation benefits. However, dynamically scheduling food preparation and delivery is challenging and we propose operational strategies for the effective operations of ghost kitchens. We model the problem as a sequential decision process. For the complex, combinatorial decision space of scheduling order preparations, consolidating orders to trips, and scheduling trip departures, we propose a large neighborhood search procedure based on partial decisions and driven by analytical properties. Within the large neighborhood search, decisions are evaluated via a value function approximation, enabling anticipatory and real-time decision making. We show the effectiveness of our method and demonstrate the value of ghost kitchens compared to conventional meal delivery systems. We show that both integrated optimization of cook scheduling and vehicle dispatching, as well as anticipation of future demand and decisions, are essential for successful operations. We further derive several managerial insights, amongst others, that companies should carefully consider the trade-off between fast delivery and fresh food.

翻译：近年来，餐厅餐食配送业务快速增长。其运营面临的主要挑战在于：顾客需求在时间和空间上分散且随机地产生于城市各处，同时顾客期望获得及时且新鲜的配送服务。为应对这些挑战，一种名为"幽灵厨房"的新型商业模式应运而生。该模式提出在中央复合式厨房中同步进行多家餐厅的餐食制备，以充分利用订单整合的优势。然而，动态调度餐食制备与配送具有挑战性，我们为此提出了提升幽灵厨房运营效率的操作策略。我们将该问题建模为序贯决策过程。针对调度订单制备、整合订单形成配送批次、安排批次出发时间这一复杂组合决策空间，我们提出了一种基于局部决策、由解析性质驱动的大邻域搜索方法。在大邻域搜索框架内，通过价值函数近似评估决策质量，从而实现前瞻性与实时决策。我们证明了该方法的有效性，并展示了幽灵厨房相较于传统餐食配送系统的价值。研究表明：烹饪调度与车辆调度的集成优化，以及对未来需求与决策的预判，对成功运营至关重要。我们进一步得出若干管理启示，其中特别指出企业需审慎权衡快速配送与食物新鲜度之间的平衡关系。