Online Learning and Pricing for Service Systems with Reusable Resources
Abstract: Online Learning and Pricing for Service Systems with Reusable Resources
We consider a price-based revenue management problem with finite reusable resources over a finite time horizon $T$. Customers arrive following a price-dependent Poisson process and each customer requests one unit of $c$ homogeneous reusable resources. If there is an available unit, the customer gets served within a price-dependent exponentially distributed service time; otherwise, the customer waits in a queue until the next available unit. We assume that the firm does not know how the arrival and service rates depend on posted prices, and thus it makes adaptive pricing decisions in each period based only on past observations to maximize the cumulative revenue. Given a discrete price set with cardinality $P$, we propose two online learning algorithms, termed Batch Upper Confidence Bound (BUCB) and Batch Thompson Sampling (BTS), and prove that the cumulative regret upper bound is $\tilde{O}(\sqrt{PT})$, which matches the regret lower bound. In establishing the regret, we bound the transient system performance upon price changes via a novel coupling argument, and also generalize bandits to accommodate sub-exponential rewards. We also extend our approach to a continuous price setting with contextual information and also a network revenue management setting.
Speaker Bio
Cong Shi is an associate professor at the Herbert Business School, University of Miami. His recent research is focused on the design and analysis of online learning algorithms for stochastic optimization models in operations management. Main areas of applications include revenue management, supply chain management, service operations, and human-robot interaction. He received his Ph.D. in Operations Research at MIT in 2012, and his B.S. in Mathematics from the National University of Singapore in 2007.