The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

In many industries, managers face the problem of selling a given stock of items by a deadline We investigate the problem of dynamically pricing such inventories when demand is price sensitive and stochastic and the firm's objective is to maximize expected revenues Examples that fit this framework include retailers selling fashion and seasonal goods and the travel and leisure industry, which markets space such as seats on airline flights, cabins on vacation cruises, and rooms in hotels that become worthless if not sold by a specific time

We formulate this problem using intensity control and obtain structural monotonicity results for the optimal intensity resp, price as a function of the stock level and the length of the horizon For a particular exponential family of demand functions, we find the optimal pricing policy in closed form For general demand functions, we find an upper bound on the expected revenue based on analyzing the deterministic version of the problem and use this bound to prove that simple, fixed price policies are asymptotically optimal as the volume of expected sales tends to infinity Finally, we extend our results to the case where demand is compound Poisson; only a finite number of prices is allowed; the demand rate is time varying; holding costs are incurred and cash flows are discounted; the initial stock is a decision variable; and reordering, overbooking, and random cancellations are allowed

https://www0.gsb.columbia.edu/mygsb/faculty/research/pubfiles/3943/vanryzin_optimal_dynamic_pricing.pdf

Optimal dynamic pricing of inventories with stochastic demand over finite horizons

This survey reviews the forty-year history of research on transportation revenue management (also known as yield management). We cover developments in forecasting, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. The survey includes a glossary of revenue management terminology and a bibliography of over 190 references.

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Revenue Management: Research Overview and Prospects

The benefits of dynamic pricing methods have long been known in industries, such as airlines, hotels, and electric utilities, where the capacity is fixed in the short-term and perishable. In recent years, there has been an increasing adoption of dynamic pricing policies in retail and other industries, where the sellers have the ability to store inventory. Three factors contributed to this phenomenon: (1) the increased availability of demand data, (2) the ease of changing prices due to new technologies, and (3) the availability of decision-support tools for analyzing demand data and for dynamic pricing. This paper constitutes a review of the literature and current practices in dynamic pricing. Given its applicability in most markets and its increasing adoption in practice, our focus is on dynamic (intertemporal) pricing in the presence of inventory considerations.

Dynamic Pricing in the Presence of Inventory Considerations: Research Overview, Current Practices, and Future Directions

Customer choice behavior, such as buy-up and buy-down, is an important phenomenon in a wide range of revenue management contexts. Yet most revenue management methodologies ignore this phenomenon - or at best approximate it in a heuristic way. In this paper, we provide an exact and quite general analysis of this problem. Specifically, we analyze a single-leg reserve management problem in which the buyers' choice behavior is modeled explicitly. The choice model is very general, simply specifying the probability of purchase for each fare product as a function of the set of fare products offered. The control problem is to decide which subset of fare products to offer at each point in time. We show that the optimal policy for this problem has a quite simple form. Namely, it consists of identifying an ordered family of "efficient" subsets S 1 ,..., S m , and at each point in time opening one of these sets S k , where the optimal index k is increasing in the remaining capacity x and decreasing in the remaining time. That is, the more capacity (or less time) available, the further the optimal set is along this sequence. We also show that the optimal policy is a nested allocation policy if and only if the sequence of efficient sets is nested, that is S 1 ? S 2 ?... ? S m . Moreover, we give a characterization of when nesting by fare order is optimal. We also develop an estimation procedure for this setting based on the expectation-maximization (EM) method that jointly estimates arrival rates and choice model parameters when no-purchase outcomes are unobservable. Numerical results are given to illustrate both the model and estimation procedure.

/pdf/revenue-management-under-a-general-discrete-choice-model-of-2vdsjyi74w.pdf

Revenue Management Under a General Discrete Choice Model of Consumer Behavior

Many industries face the problem of selling a fixed stock of items over a finite horizon. These industries include airlines selling seats before planes depart, hotels renting rooms before midnight, theaters selling seats before curtain time, and retailers selling seasonal goods such as air-conditioners or winter coats before the end of the season. Given a fixed number of seats, rooms, or coats, the objective for these industries is to maximize revenues in excess of salvage value. When demand is price sensitive and stochastic, pricing is an effective tool to maximize revenues. In this paper we address the problem of deciding the optimal timing of a single price change from a given initial price to either a given lower or higher second price. Under mild conditions, we show that it is optimal to decrease resp., to increase the initial price as soon as the time-to-go falls below resp., above a time threshold that depends on the number of yet unsold items.

Optimal starting times for end-of-season sales and optimal stopping times for promotional fares

This article studies a continuous-time yield management model in which reversible price changes are allowed. We assume that perishable assets are offered at a set of discrete price levels. Demand at each level is a Poisson process. To maximize the expected revenue, management controls the price dynamically as sales evolve. We show that a subset of these prices that form a concave envelope is potentially optimal. We formulate the problem into an intensity control model and derive the optimal solution in closed form. Properties of the optimal solution and their policy implementations are discussed. Numerical examples are provided.

A Continuous-Time Yield Management Model with Multiple Prices and Reversible Price Changes

Many industries face the problem of selling a fixed stock of items over a finite horizon. These industries include airlines selling seats before planes depart, hotels renting rooms before midnight, theaters selling seats before curtain time, and retailers selling seasonal items with long procurement lead times. Given a sunk investment in seats, rooms, or winter coats, the objective for these industries is to maximize revenues in excess of salvage value. When demand is price sensitive and stochastic, pricing is an effective tool to maximize expected revenues. In this paper we address the problem of deciding the optimal timing of price changes within a given menu of allowable, possibly time dependent, price paths each of which is associated with a general Poisson process with Markovian, time dependent, predictable intensities. We show that a set of variational inequalities characterize the value functions and the optimal (possibly random) time changes. In addition, we develop an efficient algorithm to compute the optimal value functions and the optimal pricing policy.

Perishable Asset Revenue Management with Markovian Time Dependent Demand Intensities

It is a common practice for industries to price the same products at different levels. For example, airlines charge various fares for a common pool of seats. Seasonal products are sold at full or discount prices during different phases of the season. This article presents a model that reflects this yield management problem. The model assumes that (1) products are offered at multiple predetermined prices over time; (2) demand is price sensitive and obeys the Poisson process; and (3) price is allowed to change monotonically, i.e., either the markup or markdown policy is implemented. To maximize the expected revenue, management needs to determine the optimal times to switch between prices based on the remaining season and inventory. Major results in this research include (1) an exact solution for the continuous-time model; (2) piecewise concavity of the value function with respect to time and inventory; and (3) monotonicity of the optimal policy. The implementation of optimal policies is fairly facile because of the existence of threshold points embedded in the value function. The value function and time thresholds can be solved with a reasonable computation effort. Numerical examples are provided.

Youyi Feng

Papers

Optimal starting times for end-of-season sales and optimal stopping times for promotional fares

A Continuous-Time Yield Management Model with Multiple Prices and Reversible Price Changes

Perishable Asset Revenue Management with Markovian Time Dependent Demand Intensities

Optimal Policies of Yield Management with Multiple Predetermined Prices

Integration of pricing and capacity allocation for perishable products