چكيده به لاتين
In the business environment, new tools have been developed to understand market needs, including dynamic pricing methods. With the advent of e-commerce, dynamic pricing has found a special place, so that in some small and medium-sized Internet businesses, the prices of some products and services are now automatically changing as the market changes. In most markets, demand and supply fluctuate, creating a constantly changing market environment. It is impossible to predict all possible future conditions of such a market, and the available information is limited. As a result, a significant amount of articles has emerged about the dynamic pricing of computer science and the artificial intelligence community. These models enable companies to put existing data into a vision and change their pricing strategy to best adapt to the market environment. A review of the literature related to research and classification of related articles found that most articles and research in the field of dynamic pricing in online businesses, using machine learning approaches, focused on commodity markets such as online commodity stores. The aim of this study is to use the reinforcement learning approach to strengthen the implementation of dynamic pricing in the AloPeyk on-demand delivery system. The Q-learning algorithm is used to solve the pricing problem in this study. Due to the large space of the states in this study, the idea of deep Q networks (DQN) is used to approximate the values of Q. Therefore, after dividing the city of Tehran, each part is assigned a coefficient by the surge multiplier decision-making agent at each time period. These coefficients are multiplied by the base price of the AloPeyk and then the final price calculated. Using market simulation using real-time AloPeyk data and implementing reinforcement learning methods in this environment and comparing the results with the real environment, the proposed approach demonstrates good performance to balance the components of on-demand delivery systems through dynamic pricing.
