7th Annual INFORMS Pricing and Revenue Management
Conference
Universitat Pompeu Fabra, June 28-29 2007, Barcelona, Spain
FINAL SCHEDULE
TRACK A: ROOM 40.002
TRACK B: ROOM 40.010
TRACK C: ROOM 40.012
KEYNOTES: AUDITORI
LUNCHES & BREAKS: PATIO
GALA DINNER: HOTEL ARTS (SEE ATTACHED MAP)
THE LAST SPEAKER IN EACH SESSION IS THE SESSION CO-ORDINATOR
June 28
Day 1 Planning
9:00-9:30
Opening/Welcome
Andreu Mas-Colell, Professor of Economics UPF
9:30-10:00
10:00-11:30
Break
STRATEGIC ISSUES +
APPLICATIONS
DEMAND MODELS + CB+
FORECASTING
PRICING & RM
OPTIMIZATION
Session A1
Competition in RM
Session B1
Strategic Buyers:
Models and Empirical
Evidence
Saving Seats:
Reservation Policies
with Strategic
Consumers, Lariviere
Session C1
Network RM 1
Consumer Behavior in
Waiting for Lastminute
Discounts,
Ovchinnikov,
Skurnik
Venkatesan
Milner
Cargo Capacity
Management on a
Network,
Levin
Nediak
McGill
Profit Loss due to
Price Competition in
Markets with
Differentiated
Products,
Farahat
Perakis
Learning to Price
Airline Seats under
Competition,
Collins
Thomas
1
Asymptotic Optimality
of a Network RM Model
Based on a Fixed Point
Approximation,
Yao
Ye
A Stochastic Multiple
Leader Stackelberg
Model,
de Miguel
Xu
11:4512:45
Large Scale Markdown
Optimization for Retail,
Gulcu
Sanli
Keynote
Xavier Vives, Professor of Economics and Financial Management IESE
12:45-2:15
2:30-4:00
Lunch
Session A2
Strategic Issues in
Service RM
Capacity and Value
Based Pricing for
Professional Services
Including Bundled and
Non-Bundled
Engagements,
Wardell,
Wynter
Helander
Competition in
Innovation and Pricing
for Short Life Cycle
Product,
Arslan
Kachani
Shmatov
Competition in Prices
& Service Level
Guarantees,
Johari
Weintraub
Session B2
Consumer Behavior in
RM: Models and
Empirical Evidence
A Quantification of the
Internet Price Effect,
Brunger
Price Variation
Antagonism and Firm
Pricing Policies
Pagliero
Courty
Revenue Management
for Fans,
Popescu
Rudi
4:00- 4:30
4:30-6:00
RLP Revisited,
Talluri
Session C2
RM under Customer
Choice
Top-Down vs. Bottom
up Approaches to
Behavioral Network RM,
Savard
Marcotte
Bigras
Cote
Rancourt
Schoeb
RM under Choice
Models,
Gallego
Li
Ratliff
A Column generation
Algorithm for choice
Based RM,
Vulcano
Bront
Diaz
Break
Session A3
The Value and
Future of Pricing &
RM
Session B3
RM with Endogenous
Costs
2
Session C3
Network RM 2
On the Value of
Dynamic Pricing,
Lobo
RM in e-fulfillment,
Agatz
Perspectives on the
Future of Pricing, Boyd
Integrated Pricing &
Inventory Control with
Reference Effects,
Gimpl-Heersink
Rudloff
Taudes
Dynamic Pricing of
Inventories with Menu
Costs, Feldmann 32
Capacity Reservation
in a Make-to-Order
System with Uncertain
Due-Date Preferences,
Yadav
Pibernik
6:05-7:00
Capacity-Dependent
Bid-Prices in Network
RM, Topaloglu
Kunnumkal
Network RM using Outer
Aproximation
Algorithms Philpott
Van Ryzin
Keynote
Bruno Matheu, Air France
Cocktail  Dinner
Hotel Arts
7:30-…
29 June 2007
9:00-10:30
Session A4
Supply Chain Issues
and Contracts
Competing for Shelf
Space,
Martinez de Albeniz
Roels
Competitive Supply
Chain Contracting and
Tour Revenue
Management,
Anderson
Markus
Capacity Contracts for
Cargo Carriers and
Forwarders,
Cooper
Amaruchkul
Gupta
Session B4
RM with Consumer
Behavior
Joint Memory
Dependent Pricing and
product introduction for
multiple generations
Kachani
Arslan
Shmatov
Optimal Pricing of
Services with Switching
Costs,
Liu
van Ryzin
Consumer Search
Behavior when Product
Quality is Uncertain,
van Ryzin
Debo
3
Session C4
Robust RM 2
Dynamic Revenue
Management Policies
with Limited Demand
Information
Ball
Gao
Karaesmen
Robust Newsvendor
Competition,
Jiang
Netessine
Savin
10:30-11:00
11:00-12:30
Break
Session A5
Cooperation &
Competition in RM
Session B5
Demand Models and
Forecasts
A Comparison of
Static vs. Dynamic
Schemes for Alliance
RM, Shumsky
Wright
Groenvelt
Retracing Choice
Process Among SNCF
Clients,
Wiesel
Riss
Brotcorne
Savard
The Strategic Role of
Capacity in a Joint
Inventory
Management and
Pricing Game, Adida
Perakis
A Pricing DP game,
Walczak
Forecasting Models and
Scenario Analysis,
Castejon
Leadtime-Variety
Tradeoff in Product
Differentiation,
Alptekinoglu
Corbett
Demand Modeling:
Insights Based on an
Empirical Study,
Zeevi
Besbes
Phillips
Capacity Investment
and Pricing with Multiple
Products and Production
Priorities,
Transchel
Minner
Pike
12:30-2:00
2:00-3:30
Session C5
Pricing & Inventory
Decisions for
Differentiated
Products
A Stochastic Dynamic
Programming Approach
to RM in a Make-toStock Production
System, Quante
Meyr
Fleischmann
Lunch
Session A6
RM in Service
Applications
RM in Facilities where
Customers Wait for
Service,
Giloni
Troy
RM for Online
Advertising,
Fridgeirsdottir
Araman
Session B6
Overbooking
Data Mining Based No
Show and Cancellation,
Wang
Romero
Value of Traffic Mixbased Overbooking
J. Lancaster
Munich
Graff
4
Session C6
RM with Limited
Information
Sequential Prediction
Under Imperfect
Monitoring,
Lugosi
Dynamic Pricing with
Learning,
Kleywegt
Access Pricing
Services with Positive
Externalities,
Kumar
Johari
Robust Revenue
Management:
Overbooking and FareClass Allocation
Karaesmen
Ball
Lan
3:30-4:00
4:00-5:00
Break
Session A7
The Right Approach
to RM
Risk-Sensitive
Capacity Control,
Barz
Is RM Accessible to
the Small Hotelier?
Colomines
William
Session B7
Healthcare
Pricing-Capacity
Tradeoffs in Rehab
Healthcare Networks:
Models and Open
Problems,
Millhiser
The Performance of
Nonlinear Pricing
Schemes in Medical
Service in Korea,
Kwak
Paik
Nam
Ha
5
Dynamic Pricing Using
Adjustable Optimization,
Lobel
Perakis
Session A1: 28 June 10:00-11:30
Room 40.002
Session Title: Competition in RM
6
Learning to Price Airline Seats under Competition
A.J.Collins, Lyn Thomas
University of Southampton
Applied Game Theory is criticised for not being able to model real decision making
situations. A game’s sensitive nature and the difficultly in determining the utility
payoff functions make it hard for a decision maker to rely upon any game theoretic
results. Therefore the models tend to be simple due to the complexity of solving
them (i.e. finding the equilibrium). In recent years, due to the increases of
computing power, different computer modelling techniques have been applied in
Game Theory. A major example is Artificial Intelligence methods e.g. Genetic
Algorithms, Neural Networks and Reinforcement Learning (RL). These techniques
allow the modeller in incorporate Game Theory within their models (or simulation)
without necessary knowing the optimal solution. After a warm up period of repeated
episodes is run, the model “learns” to play the game well (though not necessary
optimally). This is a form of simulation-optimization. The objective of the research is
to investigate the practical usage of RL within a simple sequential stochastic airline
seat pricing game. Different forms of RL are considered and compared to the
optimal policy, which is found using standard dynamic programming techniques. The
airline game and RL methods displays various interesting phenomena, which are also
discussed. For completeness, convergence proofs for the RL algorithms are being
constructed.
Session A1: 28 June 10:00-11:30
Room 40.002
7
Session A1: 28 June 10:00-11:30
Room 40.002
8
Session B1: 28 June 10:00-11:30
Room 40.010
Session Title: Strategic Buyers: Models and Empirical Evidence
Saving Seats: Reservation Policies with Strategic Customers
Martin A. Lariviere, Northwestern University
We consider a service provider such as a restaurant that offers reservations. The
firm serves two customer segments. The size of both segments is random. The first
market segment demands reservations. These customers contact the service
provider before the date that service is required and will only patronize the firm if
they are given a reservation that guarantees access to the provider’s limited
capacity.The second segment is made up of walk-in customers who can only access
the firm at the time that service is required. Walk-in customers face a fixed cost in
reaching the service provider and cannot determine whether capacity is available
before incurring the fixed cost. Consequently, whether a walk-in customer actually
attempts to patronize the firm depends on the amount of capacity she expects to be
free. Hence, the number of reservations made available to the first segment
determines the amount of demand from late arriving walk-in customers. We examine
this trade off and show that the firm may find it optimal to commit to turning away
early arriving customers even when these customers offer a higher margin.
9
Session B1: 28 June 10:00-11:30
Room 40.010
10
Large Scale Markdown Optimization for Retail
Altan Gulcu, Tugrul Sanli
altan.gulcu@sas.com
100 SAS Campus Drive, Cary, NC, 27513 USA
Tight profit margins and intense competitive pressures require that retailers
maximize the returns from their end-of-season and end-of-life merchandise. Taking
markdowns too often, at the wrong time, on the wrong items or at the wrong
location can be costly. Moreover, such markdowns are often priced too low, resulting
in lower margin; or too high, resulting in lost revenues, leftover inventory and higher
labor costs. While retailers often view markdowns as a necessary activity to clear
merchandise, when executed properly, they can deliver significant lifts in demand
and profitability.
SAS Markdown Optimization provides retailers the ability to determine which items
should be marked down, by how much they should be marked down, when, and in
which markets, stores or store clusters. Retailers of all kinds—apparel, mass,
specialty, hard goods, food, drug—can base this analysis on critical factors such as
inventory levels, base sales volume, price elasticity and local demand or preferences.
It helps retailers develop and implement optimal pricing strategies at the local
market level to maximize revenues and profitability while meeting end-of-season
inventory goals.
We model the markdown optimization problem with multiple objectives,
incorporating business rules and solve it via dynamic programming and a customized
search algorithm. The model allows the retailer to specify and force uniform pricing
rules at store clusters. Our presentation will focus on the modeling and formulation
of the business problem, input required for and the output generated by the model
and solution methodologies used.
Session B1: 28 June 10:00-11:30
Room 40.010
11
Session C1: 28 June 10:00-11:30
Room 40.012
Session Title: Network RM 1
12
Capacity Management on a Network
Yuri Levin, Tatsiana Levina, Jeff McGill, Mikhail Nediak
Queen’s University
We consider a cargo shipping problem on a network of locations connected by flights
operated on a fixed periodic schedule. Bookings for cargo shipping between origindestination pairs are made in advance. However, cargo capacity availability as well
its utilization by each package are unknown at the time of booking. The problem is to
maximize the expected present value of profit by controlling accept/reject decisions
for booking requests and dispatching accepted packages through the network. We
discuss the structural properties of the optimal policy and numerical approximation
schemes.
RLP Revisited
Kalyan Talluri
ICREA & Universitat Pompeu Fabra
Empirically, tighter upper bounds to the dynamic programming value function seem
to lead to better bid-prices – i.e., better revenues. So obtaining tighter upper bounds
is a worthwhile research goal. The Randomized Linear Programming method for
Network RM is a very simple and effective method that is based on the Perfect
Hindsight (PHS) upper bound obtained from simulating the forecasts. In this work
we related the PHS upper bound to recent work by Adelman and the Lagrangian
bound of Topaloglu. We also extend our work to choice network RM.
Session C1: 28 June 10:00-11:30
Room 40.012
13
Session A2: 28 June 2:30-4:00
Room 40.002
Session Title: Strategic Issues in Service RM
14
COMPETITION IN INNOVATION AND PRICING
FOR SHORT LIFE-CYCLE PRODUCTS
Hasan Arslan*
Soulaymane Kachani
Kyrylo Shmatov
Fast-changing consumer preferences and high pace of product and process
innovation together with intense competition force companies to introduce new
products quickly at ever-increasing rates. This leads to proliferation of products with
short life-cycles in the apparel and high-tech (consumer electronics, PC, software)
industries. Firms in these industries become more vulnerable to competition as
product life-cycles decrease. Introducing the right product at the right time and
applying the right pricing mechanism are extremely challenging tasks for these
companies. While some firms focus on catering to consumer tastes by quickly
substituting new products and phasing out the old products, others focus on
capturing consumer surplus through frequent price discounts. Firms that have quick
turnaround on products are likely to attract more innovation- or fashion- sensitive
consumers, whereas firms that execute price discounts over products' life-cycles
tend to attract more price-sensitive consumers.
In this research, we consider the competition among firms that produce and sell
short life-cycle products to consumers who are segmented in terms of price
sensitivity and their purchasing behavior towards new and innovative products. Our
objective is to derive insights on how pricing and product innovation strategies
impact these firms' market positioning. We assume that the market demand for a
newly introduced short life-cycle product responds to two main factors: deviation of
the product's own price from a market-wide reference price, which is essentially an
exponentially-weighted moving average of past prices, and deviation of the product's
innovation (fashion) level from the current market average. To the best of our
knowledge, our research is the first to incorporate the joint memory effects both in
pricing and the product innovation level. In terms of market positioning, we consider
three types of firm strategies: (i) price discounts and promotional sales, (ii) high
product introduction rate to gain a competitive edge by developing new products and
restocking faster than others, and (iii) a hybrid strategy that embeds both
approaches.
We study the competition among firms under a variety of market conditions through
a differential game. This differential game-theoretic model accounts for different
types of consumer segments and different types of pricing and product innovation
strategies of competing firms. Firms producing short life-cycle products may not
have enough time to update their measures of market reference price and reference
product innovation level. Therefore, we argue that open-loop strategies in practice fit
well to such firms. We first derive open-loop Nash equilibria for an oligopoly game
and show that the solution is a linear combination of exponentials. We then extend
our analysis to the corresponding closed-loop Nash equilibria. We expected that
open-loop and closed-loop Nash equilibrium solutions would not differ much. We
confirm this intuition and quantify, both analytically and numerically, how the
difference between the two equilibria depends on the demand sensitivity parameters,
and how this difference is small for reasonably low values of these parameters.
*
Sawyer Business School, Suffolk University, Boston, MA, harslan@suffolk.edu
IEOR Department, Columbia University, New York, NY, kachani@ieor.columbia.edu

APAM Department, Columbia University, New York, NY, kis2101@columbia.edu

15
We focus on characterization of distinct classes of firms in the market in terms of
their pricing and product innovation strategies. Specifically, we consider two main
classes: discounter firms that control their pricing policy while keeping their product
innovation level constant (no improvement of offered products and no new
generation development), and high product introduction rate firms that assume a
non-trivial product innovation policy while executing a constant pricing policy. We
analyze specific competition among different firms that adhere to different types of
pricing and product innovation strategies, and derive the corresponding Nash
equilibria in these settings.
To derive insights on competition among different market positioning strategies, we
conduct a real-life case study to compare actual profit figures, achievable under
different competition scenarios in different types of market, against the historically
realized figures. This shows the level of improvement that may be achieved if more
informed pricing and product innovation strategies are adopted. The case study is
based on the example of competing retailers in the apparel industry - one that gains
a competitive edge through high product introduction rate (e.g., Inditex) and its
competitor (e.g., H&M), which competes largely through promoting price discounts.
Session A2: 28 June 2:30-4:00
Room 40.002
16
Session A2: 28 June 2:30-4:00
Room 40.002
17
Session B2: 28 June 2:30-4:00
Room 40.010
Session Title: Consumer Behaviour in RM: Models and Empirical Evidence
A Quantification of the Internet Price Effect
Bill Brunger, Case Western
Customers who use Internet/Online Travel Agencies to purchase “clearly
leisure” trips (i.e. trips which were booked at least 14-days in advance, where the
passenger stayed over the weekend) pay significantly less (11.5% in our sample,
displayed below) for similar itineraries in the same markets than those who purchase
through traditional travel agencies even though the fares and inventory offered by
the airline are identical because of contractual obligations and strategic policy. The
purpose of this study is to examine this Internet Price Effect, explanations for which
may lie in secondary market segmentation, differences in characteristics of trips
chosen, and search efficiencies.
A Comparison of Average Fare Paid for Comparable Itineraries by Leisure
Passenger
(Airline proprietary data)
Fare Paid for "clearly LEISURE" Itinerary
(Net of all agency fees charged to the customer)
February '06
/
EWR-RDU
EWR-PHX
EWR-LAX
EWR-ORL
IAH-SEA
IAH-ORD
IAH-LGA
CLE-SFO
Traditional TA
CLE-LAS
EWR-RDU
EWR-PHX
EWR-LAX
EWR-ORL
IAH-SEA
IAH-ORD
IAH-LGA
CLE-SFO
CLE-LAS
Internet Agency
June '06
Specifically, relying on the Literatures of Customer Behavior/Internet Search
and the techniques and precedents of previous price and price dispersion studies,
this study will use detailed passenger-level trip data (previously unreported in the
Literature) to examine differences across the distribution channels, while controlling
for customer, market and flight characteristics and seat value, in order to explore
and attempt to quantify the residual impact of Internet search.
At the individual level, the Regression Equation should be of the form:
FP= ß0 + ß1*DC + ß2*TC+ ß3*TD + ß4*MS + ß5*OpV + ε
Where FP is the Fare Paid (as a percent of the mean fare paid for a similar trip in the
same market), DC is Distribution Channel, TC, TD and MS are matrices of variables
related to Trip Characteristics, Traveler Demographics and Market Structure, OpV is
the Opportunity Value of the seat used and ε is an error term.
18
Reserve University will present his research design and preliminary findings, and
solicit thoughts on how to proceed with the study.
Session B2: 28 June 2:30-4:00
Room 40.010
19
Session B2: 28 June 2:30-4:00
Room 40.010
20
Session C2: 28 June 2:30-4:00
Room 40.012
Session Title: RM under customer choice
21
Revenue Management under Choice Models
Guillermo Gallego, Lin Li and Richard Ratliff
New revenue management heuristics are described to control capacity availability for
the multi-fare, single-leg problem under time dependent choice models. The model is
rich enough to allow for downsell, upsell, competition effects and market segments
with different sensitivities to price and fare attributes. Both dynamic and static
heuristics are described. The heuristics are computationally as simple as applying
EMSR-b with buy up, but perform nearly as well as solving the full dynamic
programming problem
A column generation algorithm for choice-based network revenue
management
Gustavo Vulcano (New York University, USA)
Juan Jose Miranda Bront and Isabel Mendez Diaz (University of Buenos Aires,
Argentina)
In the last few years, there has been a trend to enrich traditional revenue
management models built upon the independent demand paradigm by accounting for
customer choice behavior. This extension involves both modeling and computational
challenges. One way to describe choice behavior is to assume that each customer
belongs to a segment, which is characterized by a consideration set, i.e., a subset of
the products provided bythe firm that a customer views as options. Customers
choose a particular product according to a multinomial-logit criterion, a model widely
used in the marketing literature. In this paper, we consider the choice-based,
deterministic, linear programming model (CDLP) of Gallego et al (2004), and the
follow-up dynamic programming (DP) decomposition heuristic of van Ryzin and Liu
(2004),and focus on the more general version of these models, where customers
belong to overlapping segments. To solve the CDLP for real-size networks, we need
to develop a column generation algorithm. We prove that the associated column
generation subproblemis indeed NP-Complete, and propose a simple, greedy
heuristic to overcome the complexity of an exact algorithm. Our computational
results show that the heuristic is quite effective, and that the overall approach has
good practical potential and leads to high quality solutions.
Session C2: 28 June 2:30-4:00
Room 40.012
22
Session A3: 28 June 4:30-6:00
Room 40.002
Session Title: Value and Future of Pricing and RM
23
Perspectives on the Future of Pricing
Andy Boyd, PROS
Every company that exists sells something, and in doing so sets a price for what it
sells. This gives rise to a variety of ways to “price” and “sell,” many of which have
received little research attention. We begin by taking a look at demand curves (price
response functions) and their practical and theoretical limitations. We then survey
some business models where the use of demand curves is inappropriate. Special
attention is given to actual methods of pricing that can benefit from the application
of mathematical modeling.
Capacity Reservation Levels in a Make-To-Order System with Uncertain DueDate Preferences
Richard Pibernik and Prashant Yadav
MIT-Zaragoza International Logistics Program
We consider a Make-to-Order manufacturer facing random demand from two classes
of customers. We develop an integrated model for reserving capacity in anticipation
of future order arrivals from high priority customers and setting due dates for
incoming orders. Our research exhibits two distinct features: (1) we explicitly model
the manufacturer’s uncertainty about the customers’ due date preferences for future
orders and (2) we utilize a service level measure for reserving capacity rather than
estimating short and long term implications of due date quoting with a penalty cost
function. We identify an interesting effect (“t-pooling”) that arises when the (partial)
knowledge of customer due date preferences is utilized in making capacity
reservation and order allocation decisions. We characterize the relationship between
the customer due date preferences and the required reservation quantities and show
that not considering the t-pooling effect (as done in traditional capacity and
inventory rationing literature) leads to excessive capacity reservations. Numerical
analyses are conducted to investigate the behavior and performance of our capacity
reservation and due date quoting approach in a dynamic setting with multiple
planning horizons and roll-overs. One interesting and seemingly counterintuitive
finding of our analyses is that under certain conditions reserving capacity for high
priority customers not only improves high priority fulfillment, but also increases the
overall system fill rate.
Session A3: 28 June 4:30-6:00
Room 40.002
24
Session B3: 28 June 4:30-6:00
Room 40.010
Session Title: RM with Endogenous Costs
25
Session B3: 28 June 4:30-6:00
Room 40.010
26
Dynamic Pricing of Inventories with Menu Cost
Gunnar Feldmann, University of Massachusetts
The exchange of goods and service is affected by pricing policies. There are two
broad categories: posted-price mechanisms (take-it-or-leave-it pricing), and pricediscovery mechanisms (auction pricing). In the past companies fixed prices of a
product or service for a relative long time period; i.e, the prices were considered
static. The reason for this strategy is found in the absence of accurate demand
information, high transaction costs associated with changing prices, and huge
investment in software and hardware to implement a dynamic pricing strategy.
Although, dynamically posted prices are also take-it-or-leave-it prices the seller can
dynamically change prices over time. The goal here is to balance demand and
supply. Early adopters of dynamic pricing methods are commonly found in industries
where the short-term capacity is difficult to change such as airlines, cruise ships, and
hotels. Most of these industries operate in a centralized fashion which allows prices
to be changed at little or no cost. Contrasting to the former are retail like industries,
short-term supply is more flexible but price changes are costly. Hence the focus has
been on improving inventory management practices. Only recently there has been an
increased interest in adaptive pricing strategies. What has not been completely
addressed in the literature though is the cost of changing prices in conjunction with
inventory management. Costs associated with changing production rates (set up
costs, new equipment, etc.) have been extensively studied. However, there is often
also a cost associated to changing prices, for example, costs to update computer
databases, to change posted price in the store, to send out advertising fliers, to buy
radio advertising time, and so on. This type of cost is commonly referred to as menu
cost. In order to compare the change in profit resulting from dynamic pricing
policies, menu cost must be considered in the total cost formulation. In this paper we
are particularly interested in the optimization of net profit in settings of constraint
capacity or supply chains with high order costs which do not allow for responsive
production/order changes in the face of demand uncertainty. Therefore, the
motivation of this research effort is to make progress towards developing an
effective method of demand adaptive pricing with menu cost. Our approach is similar
to an(S,s) policy where prices are changed once the inventory goes above or below a
certain threshold value.
Session B3: 28 June 4:30-6:00
Room 40.010
27
Session C3: 28 June 4:30-6:00
Room 40.010
Session Title: Network RM 2
28
Network revenue management optimization using outer approximation
algorithms
Andy Philpott
(Joint work with Garrett. van Ryzin and Michael Frankovich)
We study dynamic bid-pricing policies in network revenue management problems.
The policies are derived by computing an outer approximation to the optimal value
function of the deterministic linear program that arises from considering the problem
over the remainder of the decision horizon. The outer approximation results in an
algorithm that triggers a re-optimization of the deterministic linear program when a
threshold condition is reached. Using numerical simulations, the trigger policy is
compared with a multi-stage stochastic programming approach to computing an
optimal bid-pricing policy.
Session C3: 28 June 4:30-6:00
Room 40.012
29
Session A4: 29 June 9:00-10:30
Room 40.002
Session Title: Supply Chain Issues and Contracts
30
Session A4: 29 June 9:00-10:30
Room 40.002
31
Session A4: 29 June 9:00-10:30
Room 40.002
32
Session B4: 29 June 9:00-10:30
Room 40.010
Session Title: RM with customer behavior
JOINT MEMORY-DEPENDENT PRICING AND PRODUCT
INTRODUCTION FOR MULTIPLE GENERATIONS
Hasan Arslan*
Soulaymane Kachani
Kyrylo Shmatov
Fierce competition and rapid innovation in technology intensive and fast-changing
markets force many firms to introduce new generations of products at ever
increasing rates. Firms adopt multiple generation product development framework,
and compete to extend their product offerings through advancement of their product
or process technology. As the rate of new generation product introductions
increases, product life cycles in many industries such as electronics, personal
computers, cellular phones, software, and toys become shorter, which magnifies the
importance of managing both the development and sales of new generation products
for manufacturing firms. Therefore, manufacturing firms face important strategic
questions of when to introduce the new generation product, when to phase out the
old generation product, what pricing mechanism to follow for the old generation
product before and after the introduction of the new generation, and what pricing
mechanism to follow for the new generation product before and after phasing out the
old one. Answers to these questions significantly impact the survival and success of
the firms.
Since manufacturing firms benefit from introducing multiple generations of products,
it is very helpful to understand the dynamics of product transition from one
generation to the next, and to develop a strategic plan on when to bring new
generation products to the market and how to price them over their product lifecycles. In our research, we analyze how to efficiently manage the development and
sales of multiple generation products. In particular, we look at two mechanisms
jointly: product introduction timing mechanism and product pricing mechanism. To
provide insights into the dynamics of managing multiple generation products, we
consider a manufacturer that introduces successive generations of products in a
market where consumers form the reference price through their personal shopping
experience and exposure to price information.
A reference price can be defined as the internal price, to which consumers compare
the observed price (Fibich et al., Operations Research 2003). As customers visit the
store, they develop price expectations in the form of a reference price that becomes
the benchmark, against which customers compare the current price. Namely, the
reference price effect implies that differences between the reference price and the
current shelf price affect the demand for the product. Since reference price is formed
through past price exposures of consumers, the problem of optimal price strategy for
the manufacturer results in an optimal control problem in a monopolistic
environment, and a differential game in a competitive setting. Given the impact of
the formed reference price on the demand for product generations, we develop
*
Sawyer Business School, Suffolk University, Boston, MA, harslan@suffolk.edu
IEOR Department, Columbia University, New York, NY, kachani@ieor.columbia.edu

APAM Department, Columbia University, New York, NY, kis2101@columbia.edu

33
optimization models for the manufacturer to determine (i) the optimal pricing
strategies for the newly introduced product generation and the incumbent product
generations and (ii) the optimal introduction times for the newly developed product
generations. We analyze the manufacturer both in a monopoly and a duopoly
environment.
The main contributions of our research are threefold. First, we characterize pricing
and introduction time strategies explicitly for each successive product generation.
This helps us carry out sensitivity analysis for the optimal strategies to gain new
insights into the problem. Second, we generalize our model to the case of an
arbitrary number of successive generations. We show that this model can be solved
much more efficiently compared to the equivalent dynamic programming model. We
develop an efficient solution procedure to determine optimal pricing and introduction
decisions, and use it to derive important insights into the problem’s nature. Third, we
deduce the equilibrium pricing and introduction time strategies in a duopoly game
between two manufacturers. We analyze both symmetric and asymmetric
manufacturers in terms of the impact of reference price on demand. We demonstrate
the value of taking the reference price effect into account in developing both pricing
and product life-cycle decisions.
34
Consumer Search Behavior when the Product Quality is Uncertain
Laurens G. Debo, Carnegie Mellon University
Garrett J. van RyzinColumbia University
Observed inventory levels may influence customer purchasing, especially when there
is uncertainty on the quality of the product. Customers may infer from low inventory
levels that other customers have bought the product and therefore the product may
be of high quality. We analyze a model in which the equilibrium customer search and
purchasing behavior depends on the costs of information gathering, the prior of the
quality, the strength of the private information, the noise on the inventory
observations and the replenishment lead times. We discuss implications for design of
distribution networks for new, innovative, fashion or other products with uncertain
quality.
Session B4: 29 June 9:00-10:30
Room 40.010
35
Session C4: 29 June 9:00-10:30
Room 40.012
Session Title: Robust RM2
36
Robust Newsvendor Competition
Houyuan Jiang, Judge Business School, University of Cambridge
Serguei Netessine, The Wharton School of Business, University of Pennsylvania
Sergei Savin, Graduate School of Business, Columbia University
We generalize the analysis of newsvendor competition to the setting in which the
competitors only possess information about support of demand distribution but do
not know the distribution itself. We analyze and compare several approaches relying
on (relative or absolute) regret minimization or the worst-case criterion instead of
the traditional approach in which the expected profit is maximized. For a game with
an arbitrary number of players we find the unique Nash equilibrium solution for these
robust newsvendor games. We further obtain closed-form expressions for the
optimal order quantities which lead to intuitive insights into the problem. Numerical
analysis indicates that, among different robust approaches, the absolute regret
minimization offers the most sensible alternative when demand distribution is
unknown.
Session C4: 29 June 9:00-10:30
Room 40.012
37
Session A5: 29 June 11:00-12:30
Room 40.002
Session Title: Competition in RM
38
Session A5: 29 June 11:00-12:30
Room 40.002
39
Pricing DP Game
Darius Walczak, PROS Revenue Management
Abstract: We investigate two airlines in the following competitive setting. Each
airline tries to maximize expected revenue from an initial, finite inventory of seats
over a finite time horizon. The price point structure is the same for both. Customers
seek the lowest price in the market; when the prices match they make a random
choice. We allow a nonzero probability that a customer does not buy. All necessary
probabilities are known to each airline and the price points can change at each
customer request. The model is related to Dudey (1992) and Talluri (2003).
We analyze conditions needed for the existence of pure strategy equilibria in the
sense of Nash. We present realistic numerical examples including cases without pure
strategy or with multiple equilibria. We compare the equilibrium pricing to the
cooperative solution and several heuristics, focusing on those that make use of
market information modeled as an independent process (Walczak 2006).
Session A5: 29 June 11:00-12:30
Room 40.002
40
Session B5: 29 June 11:00-12:30
Room 40.010
Session Title: Demand Models and Forecasts
Retracing choice process amongst SNCF clients
E. Wiesel1&2, M. Riss1, L. Brotcorne2, G. Savard3
SNCF, Innovation & Research Department, Paris, France
Université de Valenciennes, LAMIH-ROI, Valenciennes, France
3
Polytechnique Montréal, Montréal, Québec, Canada
1
2
The price bracket of the SNCF, the French National Railway Company, is quite
extended and offers various products to its customers. At the current situation, the
databases handled by the company, hold information concerning actual booking
transactions but very few details concerning the clients who carry them out.
Nevertheless, understanding the demand is a vital element for the success of the
company which must react to the rapidly developing market of transportation.
Needless to say that the demand concerns various activities of the company:
marketing, strategy, the commercial optimization centre, client service etc.
As it concerns Revenue Management, the development of a high-performance
revenue optimization tool requires mastering forecasting and estimation techniques.
In recent models of Revenue Management it is acknowledged that the fundamental
unit of demand is the client1. It is admitted that demand is the result of a decision
process involving the clients and the available alternatives. Therefore, we must shift
our focus to models of client preferences and behaviour.
This talk will present a framework which attempts to reconstruct the choice
process made by the clients, taking account of the accessible data, historic booking
transactions and marketing surveys. Our model is based on discrete choice
methodology, a theory relying on the principle of utility maximization. Preferences
are represented by the probability a client choose a given alternative within his
choice set. These probabilities are evaluated given the choice sets of the clients and
their respective decision protocols.
We pay a specific attention to latent structures in the choice process: the
correlation between the products offered by the company, the choice set formation
and the decision protocols. We refer to generalized random utility models, a family of
models which relies on the concept of hierarchical choice trees that goes hand in
hand with a more explicit consideration of the factors that intervene in the decision
process.
According to surveys carried out by the marketing service of the SNCF and
taking account of the particularities of the company’s price bracket, we propose two
kinds of latent classes: we define different customer profiles related to the
availabilities of the transportation products (for instance, card holders vs. non card
holders) and segments (leisure, business, etc.) which refer to the decision mode.
1
G. J. van Ryzin, Demand Models, In Fifth Annual INFORMS Revenue Management and Pricing Section
Conference, Cambridge, MA, June 2005, Graduate School of Business, Columbia University.
41
Tuning up the concepts of profiles and segments we can take under consideration
the characteristics of the clients and other latent variables.
In order to obtain a tractable model we consider a linear by parameters utility
function concentrating on the attributes of the alternatives (tariff, duration, quality of
service, etc.) and choose a Logit kernel which yields a closed form probability
function. Howbeit, the structure of the model remains general as it can treat non
linear relations between the attributes and together with the other components of
the model we can simulate other distributions such as the Probit kernel.
The originality of this model lays in it capacity to adapt to the SNCF environment and
specificities, in particular, the data handled by the company. Nevertheless, this is a
generic model that can be easily adapted to other settings, not necessarily in the
transportation industry, and could be used in various fields. Moreover, this model
provides an application of discrete choice theory for the analysis of demand on a
microeconomic level.
Forecasting Models and Scenarios Analysis
FERNANDO CASTEJÓN
IBERIA L.A.E. – REVENUE MANAGEMENT
A review about the different techniques used by Iberia to provide (each week)
information about the main traffic parameters:
- ASK
- RPK
- LF
- Yield
- Revenue
- RASK
Also, a tool to study the different results (scenarios) when changes are
done in price or capacity will be showed.
Session B5: 29 June 11:00-12:30
Room 40.010
42
Session B5: 29 June 11:00-12:30
Room 40.010
43
Session C5: 29 June 11:00-12:30
Room 40.012
Session Title: Pricing and inventory for differentiated products
44
Session C5: 29 June 11:00-12:30
Room 40.012
45
Session C5: 29 June 11:00-12:30
Room 40.012
46
Session A6: 29 June 2:00-3:30
Room 40.002
Session Title: RM in Service Applications
Revenue Management for Facilities Where Customers Wait for Service
Avi Giloni, Sy Syms School of Business, Yeshiva University
Philip M. Troy, Ph.D., Decision Support Systems Analyst, Les Entreprises TROYWARE
An important component of managing revenue generating facilities where customers
wait for services is to select an implementable pricing mechanism that maximizes
revenues. Since waiting can take away from the benefit customers receive for
services, customers will generally try to ensure that the benefit they receive exceeds
the waiting costs they expect to incur plus any tolls they are charged. We assume
that customers can compute their expected waiting costs provided they can see the
jobs in front of them and the distribution of time that those jobs will take to be
serviced, or that they know the parameters characterizing arrivals and job servicing.
There have been several pricing mechanisms suggested in the literature. One
common pricing mechanism is to charge a state independent toll, i.e. a toll that does
not vary with the number of jobs in the facility. A less commonly used pricing
mechanism is to charge a state dependent toll that does vary with the number of
jobs in the facility. The latter mechanism has the advantage of being better able to
respond to different levels of waiting, arriving customers incur. Unfortunately, both
types of pricing mechanisms are limited by their inability to fully collect as revenue
the value that customers receive in excess of their costs.
With respect to state
dependent tolls, this is a well known result (see Chen and Frank, 2001), and it can
be easily demonstrated for state independent tolls as well. This limitation is most
obvious when there are just two types of customers using the facility, where the first
type has a low arrival rate but high job benefits, and the second type has a high
arrival rate and low job benefits.
An obvious approach to addressing this limitation is applying revenue management
principles to the toll selection process. Surprisingly, to the best of our knowledge,
this has not been done.
In context of this, our first goal is to apply and characterize revenue management
approaches to state dependent and independent toll mechanisms, in context of
facilities in which customers are served on a first come first served basis. We do so
both with and without assuming that facilities have the ability to perfectly
differentiate customer groups, the former to determine what is possible, and the
latter to determine what is plausible. For the purpose of analysis we rely on an
M/M/S/I queuing model, and the assumption that customer waiting costs are strictly
non-decreasing in the expected time they wait. Our approach permits us to relax the
frequently used assumptions that revenue is continuously differentiable and concave
as a function of the job submission rate. We also allow multiple servers.
Keeping in mind that state dependent pricing mechanisms appear to be more
controllable, due to their ability to specify a toll for each state, our second goal is to
characterize circumstances in which it is possible to achieve similar results using
state independent tolls as can be obtained using state dependent tolls.
We have three results.
The first is we demonstrate how to apply revenue
maximization techniques to facilities where customers wait for service for both state
dependent as well as state independent tolls. Surprisingly, when using state
independent tolls and allowing customers to see the jobs in front of them, the
47
resulting optimization problem appears too complex to solve exactly, but can be
approximately solved using a heuristic. Our second result is that the revenue
management approach for state dependent tolls, the approach that appears more
complex, is more effective, simpler, and easier to compute than for the state
independent tolls. Our third result is that the state independent toll mechanisms can
obtain similar results to those obtainable using the state dependent toll mechanism
when the increase in expected waiting costs between states is very small. This can
occur not only when waiting cost rates are low, but also when there are multiple
servers. This in turn suggests that managers wishing to use a state independent
revenue management scheme consider implementing their facility with multiple
servers.
48
REVENUE MANAGEMENT FOR ONLINE ADVERTISING
Victor Araman
Kristin Fridgeirsdottir
The Internet is a fast growing advertising medium. Companies are increasingly
taking advantage of the Internet to reach out to more customers and are allocating
an increasing portion of their marketing budget towards online advertising. We
consider a web host that generates revenues from displaying advertisements on its
website. The problem faced by the web host is quite complex with multiple ad spaces
on many webpages to be assigned to different advertisers. Each advertiser brings a
set of requirements including number of hits, size of the ad, length of the ad
campaign, types of customers to display the ad to etc. In this paper we focus on the
operational problem of the web host of effectively matching demand with supply
through pricing.
The web host faces two main uncertainties. One driven by the demand, where
advertisers approach the web host requiring a number of impressions (number of
viewers to see the ad) to be met in a certain period of time. The other one driven by
supply, which consists of the viewers visiting the web site. The web hosts seeks to
determine the advertising price that attracts the right number of advertisers given
the stream of visitors to the website so that the advertising revenues generated are
maximized.
We model the web host operation as a multi-class queueing system with multiple
servers. We assume both the advertisers’ arrivals and the viewers’ stream to be
Poisson processes. This system has interesting features different from those of
classical queueing systems. We use the model to analyze the problem faced by the
web host of determining the optimal price to charge the advertiser. Moreover, the
models developed in this paper provide a new tractable framework for analyzing the
operations of a web host.
Session A6: 29 June 2:00-3:30
Room 40.002
49
Session A6: 29 June 2:00-3:30
Room 40.002
50
Session B6: 29 June 2:00-3:30
Room 40.010
Session Title: Overbooking
51
VALUE OF TRAFFIC MIX-BASED OVERBOOKING
J. Lancaster
Munich
Graff, JDA Software
Overbooking is the most financially successful and oldest of revenue management
practices. Although overbooking is considered a solved problem in most service
industries, air cargo faces additional challenges because of the dramatic variation
between the quantities booked and the quantities actually shipped. The
effectiveness of air cargo overbooking models is heavily dependent on the quality of
the show-up rate forecasts, which are traditionally produced from leg level history.
Because show-up behavior is a characteristic of the shipper, however, rather than
the leg, intuition suggests that forecasting improvements can be achieved by
segmenting demand and modeling at customer rather than leg level.
This study tests that intuition against data provided by a U.S. air carrier, examining
the convergence rates of a simple leg-based forecaster following shifts in traffic mix.
The results are particularly interesting for revenue management, because the very
purpose of a revenue management system is to change the traffic mix. With originand-destination controls and a leg-based forecasting solution, this results in an openloop system with no effective feedback between traffic mix optimization and show-up
rate forecasting.
Session B6: 29 June 2:00-3:30
Room 40.010
52
Session B6: 29 June 2:00-3:30
Room 40.010
53
Session C6: 29 June 2:00-3:30
Room 40.012
Session Title: RM with learning
Sequential prediction under imperfect monitoring
Gabor Lugosi, ICREA & UPF
We survey some on-line forecasting techniques for sequential prediction when the
forecaster does not have access to the sequence to be predicted but rather receives
a feedback signal. We investigate when the forecaster can perform almost as well as
the best predictor defined in hindsight. We describe some stylized applications for
dynamic pricing problems.
Pricing and Buyer Learning
Anton Kleywegt, Georgia Tech
Many traditional dynamic pricing models such as the ones widely used in revenue
management assume that the demand at each point in time depends on the price at
that point in time only, that is, it is independent of prices at other points in time.
Recently some models of so-called strategic customer behavior have been studied, in
which buyers' purchasing decisions at a point in time depend on the prices at other
points in time, for example, on the sellers' pricing policies. Many new questions are
associated with such models. One question is how the buyers can be expected to
obtain and process all the information necessary to make such complicated
decisions. We study several models in which buyers learn quantities that are simpler
than the pricing policies of the sellers. We investigate the convergence of the
buyers' estimates, and compare the limits with outcomes associated with full
information-
54
Session C6: 29 June 2:00-3:30
Room 40.012
55
Session A7: 29 June 4:00-5:00
Room 40.002
Session Title: Right Approach to RM
56
Is Yield Management Accessible to the Small Hotelier?
By Robin Williams,
Lluis Colomines, Aïda Díodeplus
Contrary to popular belief within some areas of the Industry, the most successful
approach to yield is a step by step development of techniques and knowledge which
may or may not lead to an inversion in software. The starting point has to be the
acquirement of knowledge, understanding the basis of yield is what will provide any
hotelier with the foundation on which to build an effective Yield Management
process.
Once an hotelier understands those principles he will be able to put in place the
necessary process to ensure that the accurate information required is obtained on a
regular basis. Many hoteliers have had great success by using simple Excel or Access
based calculations with existing although reduced historical information and this has
given them the encouragement to continue improving these processes gradually but
effectively.
Based on this idea of starting small and building on strong foundations it would seem
that what the majority of hoteliers need today is good strong tutoring in the
principles of Yield and the possibilities available to them internally and externally,
without the need for excessive resources. Similarly, one can also consider the idea of
punctual actions or can Yield only be applied on a continual and constant basis?
Session A7: 29 June 4:00-5:00
Room 40.002
57
Session B7: 29 June 4:00-5:00
Room 40.010
Session Title: RM in Healthcare
Pricing-Capacity Tradeoffs in Rehabilative Healthcare Networks: Models and
Open Problems
William P. Millhiser, PhD
Assistant Professor of Management
Baruch College, City University of New York
A United States network of rehabilative care facilities bases the decision to accept
patients for prescheduled surgeries on bed availably and expected reimbursement.
With uncertain recovery times, there are weeks when all beds are occupied,
prescheduled surgeries are delayed and new surgery requests are rejected. Using
queueing network with blocking models, we explore relationships between a facility’s
capacity and the reimbursement thresholds used for patient acceptance/rejection.
The Performance among Nonlinear Pricing Scheme in Medical Service in
Korea
Dr. Youngsik Kwak (Jinju National University, Jinju, Korea)
Dr. Sookyung Paik (Inje University Paik Medical Center, Seoul, Korea)
Dr. Brian Y. S. Nam (Samsung OpenTide China, Beijing, China)
Dr. Hwanho Ha (Jinju National University, Jinju, Korea)
Nonlinear pricing abounds in practice because it is a potentially powerful pricing
method to explore consumer surplus.
The various forms of nonlinear pricing are
feasible within a given industry. In this context, it is important for manufacturers
and retailers to understand which nonlinear pricing scheme is appropriate to apply in
their specific situation and which nonlinear pricing schedule is the most profitable in
their market situation.
Although the merits of nonlinear pricing are well documented, the attempt to apply
nonlinear pricing in medical service in South Korea has been relatively rare.
The
researcher aims to try to full this gap by applying a practice-oriented simulation
model to health examination data. The model consists of two parts; the procedure
of calibrating the price response function for each client firm and the simulation
calculating the price points for maximizing sales volume and profit for each nonlinear
pricing scheme.
The researchers apply the model to Inje University Paik Medical Center’s health
examination data for the first time in Korean medical market. We compare the sales
volumes among nonlinear pricing scheme such as n-block tariff, two-part tariff and
all unit discount price schedule. We found that n-block tariff outperforms two-part
tariff, all unit discount price schedule, and uniform pricing.
58
Directions to Hotel Arts (DINNER ON 28 June 7:30 PM)
Take a right on Ramon Trias Fargas when you exit the University
Keep walking on Ramon Trias Fargas (towards the beach) till you see this building
(hotel Arts) on your left (5 to 10 minute walk)
Cocktail hour is in the garden
Dinner is in Sala Pau Casals
59