Optimal Dynamic Auctions for Revenue Management
Management Science
Price Guarantees in Dynamic Pricing and Revenue Management
Operations Research
Dynamic Pricing Strategies for Multiproduct Revenue Management Problems
Manufacturing & Service Operations Management
Price determination for an EOQ model for deteriorating items under perfect competition
Computers & Mathematics with Applications
Interfaces
Applying experimental online auctions in marketing research for multi-channel firms
Expert Systems with Applications: An International Journal
Real-time dynamic pricing for multiproduct models with time-dependent customer arrival rates
ACC'09 Proceedings of the 2009 conference on American Control Conference
Joint Dynamic Pricing of Multiple Perishable Products Under Consumer Choice
Management Science
Pricing and inventory management in a system with multiple competing retailers under (r, Q) policies
Computers and Operations Research
Dynamic selling of quality-graded products under demand uncertainties
Computers and Industrial Engineering
Real-Time Tactical and Strategic Sales Management for Intelligent Agents Guided by Economic Regimes
Information Systems Research
Assessing the Value of Dynamic Pricing in Network Revenue Management
INFORMS Journal on Computing
Dynamic pricing of durable products with heterogeneous customers and demand interactions over time
Computers and Industrial Engineering
| Hi-index | 0.01 |
We consider a dynamic pricing model for selling a given stock of a perishable product over a finite time horizon. Customers, whose reservation price distribution changes over time, arrive according to a nonhomogeneous Poisson process. We show that at any given time, the optimal price decreases with inventory. We also identify a sufficient condition under which the optimal price decreases over time for a given inventory level. This sufficient condition requires that the willingness of a customer to pay a premium for the product does not increase over time. In addition to shedding managerial insight, these structural properties enable efficient computation of the optimal policy.Numerical studies are conducted to show the revenue impact of dynamic price policies. Price changes are set to compensate for statistical fluctuations of demand and to respond to shifts of the reservation price. For the former, our examples show that using optimal dynamic optimal policies achieves 2.4--7.3% revenue improvement over the optimal single price policy. For the latter, the revenue increase can be as high as 100%. These results explain why yield management has become so essential to fashion retailing and travel service industries.