9/2/2023 0 Comments Dynamic pricing algorithm![]() Xiong, Y., Li, G., Fernandes, K.J.: Dynamic pricing model and algorithm for perishable products with fuzzy demand. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J.: Human-level control through deep reinforcement learning. Tesauro, G.: Temporal difference learning and td-gammon. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction (2018) Rana, R., Oliveira, F.S.: Dynamic pricing policies for interdependent perishable products or services using reinforcement learning. Rana, R., Oliveira, F.S.: Real-time dynamic pricing in a non-stationary environment using model-free reinforcement learning. ![]() Perakis, G., Sood, A.: Competitive multi-period pricing for perishable products: a robust optimization approach. Ng, A.Y., et al.: Autonomous inverted helicopter flight via reinforcement learning. Lihao, L., Zhang, J., Tang, W.: Optimal dynamic pricing and replenishment policy for perishable items with inventory-level-dependent demand. Lin, L.-J.: Self-improving reactive agents based on reinforcement learning, planning and teaching. Lim, A.E.B., Shanthikumar, J.G.: Relative entropy, exponential utility, and robust dynamic pricing. Kutschinski, E., Uthmann, T., Polani, D.: Learning competitive pricing strategies by multi-agent reinforcement learning. Könönen, V.: Dynamic pricing based on asymmetric multiagent reinforcement learning. Michael Harrison, J., Bora Keskin, N., Zeevi, A.: Bayesian dynamic pricing policies: learning and earning under a binary prior distribution. Gosavii, A., Bandla, N., Das, T.K.: A reinforcement learning approach to a single leg airline revenue management problem with multiple fare classes and overbooking. Ganti, R., Sustik, M., Tran, Q., Seaman, B.: Thompson sampling for dynamic pricing. Gallego, G., Van Ryzin, G.: Optimal dynamic pricing of inventories with stochastic demand over finite horizons. 46(5), 644–657 (2000)įeng, Y., Xiao, B.: Optimal policies of yield management with multiple predetermined prices. 185, 11–20 (2017)įeng, Y., Xiao, B.: A continuous-time yield management model with multiple prices and reversible price changes. 6(4), 346–355 (2019)įeng, L., Chan, Y.-L., Cárdenas-Barrón, L.E.: Pricing and lot-sizing polices for perishable goods when the demand depends on selling price, displayed stocks, and expiration date. 65(8), 1177–1188 (2014)ĭuan, Y., Liu, J.: Optimal dynamic pricing for perishable foods with quality and quantity deteriorating simultaneously under reference price effects. IEEE (2009)Ĭhung, J., Li, D.: A simulation of the impacts of dynamic price management for perishable foods on retailer performance in the presence of need-driven purchasing consumers. In: 2009 International Symposium on Information Engineering and Electronic Commerce, pp. 21(2) (2018)Ĭheng, Y.: Real time demand learning-based q-learning approach for dynamic pricing in e-retailing setting. 43(1), 64–79 (1997)Ĭhen, W., Liu, H., Xu, D.: Dynamic pricing strategies for perishable product in a competitive multi-agent retailers market. 107(1–2), 97–129 (2006)īitran, G.R., Mondschein, S.V.: Periodic pricing of seasonal products in retailing. 45, 148–164 (2017)Īdida, E., Perakis, G.: A robust optimization approach to dynamic pricing and inventory control with no backorders. KeywordsĪdenso-Díaz, B., Lozano, S., Palacio, A.: Effects of dynamic pricing of perishable products on revenue and waste. Our results demonstrate that the DQN based dynamic pricing algorithm generates higher revenue when compared with conventional one-step price optimization and constant pricing strategy. We show that using DQN we can model the problem of pricing perishable products. Using DQN function approximator we generalize the unseen states from the seen states, which reduces the space requirements for storing value function for each state-action combination. Hence, we use function approximation approach to address the limitations of a tabular Q-learning method. This approach is not suitable for control problems with large state spaces. The conventional tabular Q-learning method involves storing the Q-values for each state-action pair in a lookup table. The demand is influenced by the price and freshness of the product. ![]() A model-free reinforcement learning approach is used to maximize revenue for a perishable item with fixed initial inventory and selling horizon. In this paper, we address the problem of dynamic pricing of perishable products using DQN value function approximator. Dynamic pricing is a strategy for setting flexible prices for products based on existing market demand. ![]()
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