Strategic Investments, Trading, and Pricing Under Forecast Updating
Management Science
Revenue Management Through Dynamic Cross Selling in E-Commerce Retailing
Operations Research
A Monopolistic and Oligopolistic Stochastic Flow Revenue Management Model
Operations Research
Strategic Investments, Trading, and Pricing Under Forecast Updating
Management Science
Note---Pricing and Inventory Control for a Perishable Product
Manufacturing & Service Operations Management
Technical Note---Personalized Dynamic Pricing of Limited Inventories
Operations Research
Myopic Solutions of Homogeneous Sequential Decision Processes
Operations Research
An Elasticity Approach to the Newsvendor with Price-Sensitive Demand
Operations Research
The Newsvendor Problem with Advertising Revenue
Manufacturing & Service Operations Management
A Censored-Data Multiperiod Inventory Problem with Newsvendor Demand Distributions
Manufacturing & Service Operations Management
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The dynamic pricing problem concerns the determination of selling prices over time for a product whose demand is random and whose supply is fixed. We approach this problem in a novel way by formulating a dynamic optimization model in which the demand function is isoelastic but the random demand process is quite general. Ultimately, what we find is a strong parallel between the dynamic pricing problem and dynamic inventory models. This parallel leads to a reinterpretation of the dynamic pricing problem as a price-setting newsvendor problem with recourse, which is useful not only because it yields insights into the optimal solution, but also because it leads to additional insights into how pricing recourse affects the actions and profits of a price-setting newsvendor. We make contributions in three areas: First, we develop structural properties that define an optimal pricing strategy over a finite horizon and investigate how that policy impacts a newsvendor's optimal procurement policy and optimal expected profit. Second, we establish a practical and efficient algorithm for computing the optimal prices. Third, we examine how market parameters affect the optimal solution through a series of numerical experiments that utilize the algorithm.