Abstract We study price optimization of perishable inventory over multiple, consecutive selling seasons in the presence demand uncertainty. Each season consists a finite number discrete time periods, and per period is Bernoulli distributed with price-dependent parameter. The set feasible prices finite, expected corresponding to each unknown seller, whose objective maximize cumulative revenue. p...

The dynamic window approach is a well known navigation scheme developed in Fox et al. (1997) and extended in Brock and Khatib (1999). It is safe by construction and has been shown to perform very efficiently in experimental setups. However, one can construct examples where the proposed scheme fails to attain the goal configuration. What has been lacking is a theoretical treatment of the algorit...

Telecommunications industry is a highly competitive one where operators’ strategies usually rely on significantly reducing minute rate in order to acquire more subscribers and thus have higher market share. However, in the last few years, the numbers of customers are noticeably increasing leading to more stress on the network, and higher congestion rate, i.e. worse quality of service (QoS). Bec...

In this paper, we investigate the use of reinforcement learning (RL) techniques to the problem of determining dynamic prices in an electronic retail market. As representative models, we consider a single seller market and a two seller market, and formulate the dynamic pricing problem in a setting that easily generalizes to markets with more than two sellers. We first formulate the single seller...

Chapter Outline Introduction 61 Background 63 Distributional Effects of Dynamic Pricing 65 Barriers to Dynamic Pricing 69 Unfairness of Flat Rate Pricing 71 Dynamic Pricing in Other Industries 73 Overcoming the Barriers to Dynamic Pricing 74 The Effect of Dynamic Pricing on Low-Income Consumers 75 Accommodating Potential Objections 77 Conclusions 78 Appendix: Quantifying the Hedging Cost Premiu...

Dynamic pricing is the dynamic adjustment of prices to consumers depending upon the value these customers attribute to a product or service. Today’s digital economy is ready for dynamic pricing; however recent research has shown that the prices will have to be adjusted in fairly sophisticated ways, based on sound mathematical models, to derive the benefits of dynamic pricing. This article attem...

Customer behavior modeling has gained increasing attention in the context of dynamic pricing. As an important behavior phenomenon, consumer inertia refers to consumers’ inherent tendency of purchase procrastination and may induce consumers to wait even when immediate purchase is optimal from an objective perspective. This paper studies a dynamic pricing problem for a monopolist firm selling per...

This paper describes an argumentation-based approach for automating the decision making process of an autonomous agent for pricing products. Product pricing usually involves different decision makers with different possibly conflicting points of view. Moreover, when considering firms in the retail business sector, they have hundreds or thousands of products to apply a pricing policy. Our approa...