A hybrid threshold curve model for optimal yield management: Neural networks and dynamic programming

In the industrial engineering and operational research fields there is a class of perishable inventory control problems called yield management. Examples are floating pricing strategies in airlines, hotels, and the car rentals. Due to the complex nature of yield management, there are few easy-to-use models available for practical applications. A typical yield management problem for selling a single category of perishable products is to adjust the price to maximize the profit by selling as many products as possible. This paper presents a hybrid threshold curve model that integrates two approaches, neural networks and dynamic programming, for solving this type of yield management problem. According to the proposed model, threshold curves in the price–time–reservation space are generated by neural networks based on historical data or management expertise. Each point on these threshold curves defines an occurrence of sales. The probability of an occurrence can be estimated from the neural networks. The dynamic programming technique is then applied to find the optimal pricing policy to maximize the expected profit.