Multilayer feedforward networks are universal approximators
Neural Networks
Dynamic pricing and ordering decisions by a monopolist
Management Science
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Interfaces - Special issue: marketing engineering
Coordinating Clearance Markdown Sales of Seasonal Products in Retail Chains
Operations Research
Forecasting of the electric energy demand trend and monthly fluctuation with neural networks
Computers and Industrial Engineering
Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics)
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Inventory Management of a Fast-Fashion Retail Network
Operations Research
| Hi-index | 0.00 |
Markdown policies for product groups having significant cross-price elasticity among each other should be jointly determined. However, finding optimal policies for product groups becomes computationally intractable as the number of products increases. Therefore, we formulate the problem as a Markov decision process and use approximate dynamic programming approach to solve it. Since the state space is multidimensional and very large, the number of iterations required to learn the state values is enormous. Therefore, we use aggregation and neural networks in order to approximate the value function and to determine the optimal markdown policies approximately. In a numerical study, we provide insights on the behavior of markdown policies when one product is expensive, the other is cheap and both have the same price. We also provide insights and compare the markdown policies for the cases in which there is a substitution effect between products and the products are independent.