  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 consists of a Technology-Specific Bridge (TSB) that provides an
interface for communicating with restaurants or the simulator. TSB uses a PDDL
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.
