Dynamic Pricing with Neural Network Demand Models and Evolutionary Algorithms

The use of neural networks for demand forecasting has been previously explored in dynamic pricing literatures. However, not much has been done in its use for optimising pricing policies. In this paper, we build a neural network based demand model and show how evolutionary algorithms can be used to optimise the pricing policy based on this model. There are two key benefits of this approach. Use of neural network makes it flexible enough to model range of different demand scenarios occurring within different products and services, and the use of evolutionary algorithm makes it versatile enough to solve very complex models. We also compare the pricing policies found by neural network model to that found by using other widely used demand models. Our results show that proposed model is more consistent, adapts well in a range of different scenarios, and in general, finds more accurate pricing policy than the other three compared models.