Multi-Step Look-Ahead Optimization Methods for Dynamic Pricing with Demand Learning

Abstract

Dynamic pricing is a beneficial strategy for firms seeking to achieve high revenues. It has been widely applied to various domains such as the airline industry, the hotel industry, and e-services. Dynamic pricing is basically the problem of setting time-varying prices for a certain product or service for the purpose of optimizing revenue. However, a major challenge encountered when applying dynamic pricing is the lack of knowledge of the demand-price curve. The demand-price curve defines the customer's response towards changing the price. In this work, we address the dynamic pricing problem in case of unknown demand-price relation. This work introduces a less myopic pricing approach based on looking ahead for one or several future steps in our quest to optimize revenue. Specifically, the proposed formulation maximizes the summation of the immediate revenue and the expected future revenues of one or multiple look-ahead steps. A key benefit of the proposed approach is that it automatically strikes a balance between the conflicting goals of revenue maximization and demand learning, by producing less myopic and profitable prices. We provide a formulation for the presented look-ahead pricing approach, and we implement two variants of it: one-step and two-step look-ahead methods. Experiments are conducted on synthetic and real datasets to compare the proposed pricing methods to other pricing strategies in literature. The experimental results indicate that the proposed look-ahead methods outperform their counterparts in terms of the achieved revenue gain.