Reinforcement learning for dynamic pricing and capacity allocation in monetized customer wait-skipping services

We consider how to facilitate a dynamically-priced premium service option that enables customer parties to shorten their wait in a queue. Offering such a service requires that some of a business’s capacity be reserved continuously and kept ready for premium customers. In tandem with capacity reservation, pricing must be coordinated. Hence, a joint dynamic pricing and capacity allocation problem lies at the heart of this service. We propose a conceptual solution architecture and employ Proximal Policy Optimization (PPO) for dynamic pricing and capacity allocation to maximize total revenue. Simulation experiments over multiple scenarios compared PPO against a human-engineered policy and a baseline policy having no premium option. The human-engineered policy led to significantly greater revenues than the baseline policy in each scenario, illustrating the potential increase in revenues afforded by this concept. The PPO agent substantially improved upon the human-engineered policy advantage, with improvements ranging from 28% to 161%.