Cloud Kitchen: Using Planning-based Composite AI to Optimize Food Delivery Processes
This work addresses efficiency challenges for restaurants in the global food delivery market, but it is incremental as it applies existing Vehicle Routing Problem with Time Windows methods to this domain.
The paper tackles the problem of optimizing food delivery processes by developing the Cloud Kitchen platform, which uses a planning-based composite AI approach to allocate orders to vehicles and sequence deliveries, resulting in improved customer satisfaction by reducing delayed deliveries as demonstrated on a real-world dataset.
The global food delivery market provides many opportunities for AI-based services that can improve the efficiency of feeding the world. This paper presents the Cloud Kitchen platform as a decision-making tool for restaurants with food delivery and a simulator to evaluate the impact of the decisions. The platform contains a Technology-Specific Bridge (TSB) that provides an interface for communicating with restaurants or the simulator. TSB uses a planning domain model to represent decisions embedded in the Unified Planning Framework (UPF). Decision-making, which concerns allocating customers' orders to vehicles and deciding in which order the customers will be served (for each vehicle), is done via a Vehicle Routing Problem with Time Windows (VRPTW), an efficient tool for this problem. We show that decisions made by our platform can improve customer satisfaction by reducing the number of delayed deliveries using a real-world historical dataset.