Development of a reinforcement learning-based adaptive scheduling algorithm for commercial smart kitchens

Reinforcement learning (RL) is a machine learning method in which a model optimizes its decision-making strategy based on rewards or penalties received for the actions it takes in an environment, often simulated. An example of an optimized process could be work scheduling in a restaurant, with the cost function being the absolute error of the difference between the scheduled and actual delivery times of an order. In task planning, RL stands out for its ability to handle problems requiring a complex sequence of actions, where traditional planning algorithms may struggle. RL models can effectively explore the solution space, adjusting their decisions to changing conditions, which enables dynamic and adaptive task execution management. RL is a broad class encompassing various approaches to achieving a goal, and in this research, we focus on selected ones. Three popular RL methods named DQN, SARSA and TD-AC have been implemented and evaluated. The study was conducted in a simulated environment designed to replicate a "delivery-based" restaurant business model. The kitchen simulation model has been developed based on 65,845 recorded food preparation processes performed in 30 restaurants located throughout Poland. A rule-based, queue-driven model (FIFO) served as the baseline for absolute quality comparison of the generated schedules. The results show that, for the defined problem, the quality of the scheduling outcomes varies significantly depending on the choice of learning algorithm. Notably, the hybrid approach performed best under simulation conditions, considerably reducing the total completion time in a scenario reflecting the operations of a small, typical restaurant.