| Summary: | 碩士 === 國立臺灣大學 === 工業工程學研究所 === 107 === This study attempts to combine dynamic programming with deep learning method in dynamic pricing and demand learning to develop a model that can make good initial decisions before planning horizon begins and conduct online learning and decision optimization. Demand learning can help the business understand consumer preferences and meet demand, but it is limited by insufficient data and environmental uncertainty to achieve the best results. Dynamic programming, although proven to be the best solution, is not universally applicable due to dimensional curses, model assumptions, and specificity limitations. The method we propose will solve the difficulties mentioned above.
In this research, we consider the problem of a dynamic pricing problem for a perishable product with a multiple period lifetime. Use a small amount of historical sales data, we construct a Long short-term memory neural network to identify unknown environments. Then we train a deep neuron network with the optimal pricing strategy to make the pricing policy. Finally, combined with Bayesian learning to improve the ability to adapt to uncertainty.
We use discrete simulation to verify the cost different between our model and optimal policies from dynamic programming in a wide variety of market environment. The result shows that our model performs well in unknown environment.
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