The Restaurant Meal Delivery Problem with Ghost Kitchens

Restaurant meal delivery has been rapidly growing in the last few years. The main operational challenges 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 facility. Ghost kitchens can bring several advantages, such as fresher food because of the synchronization of food preparation and delivery and less delay because of the consolidated delivery of orders. Exploiting these advantages requires effective operational strategies for the dynamic scheduling of food preparation and delivery. The goal of this paper is providing these strategies and investigating the value of ghost kitchens. We model the problem as a sequential decision process. For the complex decision space of scheduling order preparations, consolidating orders to trips, and scheduling trip departures, we propose a large neighborhood search (LNS) procedure based on partial decisions and driven by analytical properties. Within the LNS, decisions are evaluated via a value function approximation, enabling anticipatory and real-time decision making. In a comprehensive computational study, we demonstrate the effectiveness of our method compared with benchmark policies and highlight the advantages of ghost kitchens compared with conventional meal delivery. Funding: G. Neria’s research is partially supported by the Israeli Smart Transportation Research Center, the Council for Higher Education in Israel, and the Shlomo Shmeltzer Institute for Smart Transportation at Tel Aviv University. F. D. Hildebrandt’s research is funded by the Deutsche Forschungsgemeinschaft (DFG) German Research Foundation Grant 413322447. M. Tzur’s research is partially supported by the Israeli Smart Transportation Research Center. M. W. Ulmer’s work is funded by the DFG Emmy Noether Programme Grant 444657906. We gratefully acknowledge their support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0510 .