Dynamic Pricing (DyP) is a form of Revenue Management in which the price of a (usually) perishable good is changed over time to increase revenue. It is an effective method that has become even more relevant and useful with the emergence of Internet firms and the possibility of readily and frequently updating prices. In this paper a new approach to DyP is presented. We design adaptive dynamic pricing strategies and optimize their parameters with an Evolutionary Algorithm (EA) offline while the strategies can deal with stochastic market dynamics quickly online. We design two adaptive heuristic dynamic pricing strategies in a duopoly where each firm has a finite inventory of a single type of good. We consider two cases, one in which the average of a customer population’s stochastic valuation for each of the goods is constant throughout the selling horizon and one in which the average customer valuation for each good is changed according to a random Brownian motion. We also design an agent-based software framework for simulating various dynamic pricing strategies in agent-based marketplaces with multiple firms in a bounded time horizon. We use an EA to optimize the parameters for each of the pricing strategies in each of the settings and compare the strategies with other strategies from the literature. We also perform sensitivity a analysis and show that the optimized strategies work well even when used in settings with varied demand functions.
Additional Metadata
Keywords revenue management, dynamic pricing, multi-agent systems, pricing agents, pricing strategies, evolutionary algorithms
THEME Software (theme 1), Logistics (theme 3), Energy (theme 4)
Publisher BNAIC
Editor P. De Causmaecker , J. Maervoet , T. Messelis , K. Verbeeck , T. Vermeulen
Conference Benelux Conference on Artificial Intelligence
Note Type B paper
Citation
Ramezani, S, Bosman, P.A.N, & La Poutré, J.A. (2011). Adaptive Strategies for Dynamic Pricing Agents. In P De Causmaecker, J Maervoet, T Messelis, K Verbeeck, & T Vermeulen (Eds.), Proceedings of the 23rd Benelux Conference on Artificial Intelligence (pp. 423–424). BNAIC.