Abstracts
(Contact email addresses mentioned
where available.)
Title:
Analysis of an integrated pricing model
Authors:
Patrice Marcotte, University of Montreal
Gilles Savard, Ecole Polytechnique (Montreal)
In many industries, an underlying network structure impacts the pricing
process, and provides opportunities for increasing revenues. This is
the case not only in transportation (airlines, railways) but also in
the realm of supply chain management and telecommunications.
In our model, a firm acting in a network-based competitive environment
must set prices on the subset of arcs in its control. Each arc is
endowed with a number of attributes (price, delay, quality, etc.) and,
once prices are set, customers are routed onto paths that minimize
their disutility, expressed as a linear combination of the attributes.
We assume that the valuation of the attributes is continuously
distributed across the user population, and represented by a
multivariate probability density function. Further, the network is
subject to congestion.
In this context, we formulate the model as bilevel program where the
leader's revenue maximizing schedule takes into account the customer
reaction, which is characterized as the solution of a parametric
(infinite-dimensional) variational inequality. We propose for its
solution a two-phase algorithm. First, we show how a discretized
version of the problem leads to a mixed-integer program that can be
addressed for a global maximum by commercial software. Next, we improve
upon this solution, using a subgradient approach. Numerical experiments
are reported.
Finally, we comment on the generalization of the model to stochastic
and time-varying environments, and discuss the issue of competitors'
reaction.
Title: Revenue Management in the Context of
Dynamic Oligopolistic Network Competition
Authors:
Terry L. Friesz, Reetabrata Mookherjee and Matthew Rigdon, Penn State
University
In this paper we are concerned with devising optimal decision rules as
well as computational methods for a normal form differential
Stackelberg game in which the leader maximizes its total revenue from
spatially dispersed production of a single homogeneous commodity,
subject to a lower bound on profit. The followers are Cournot-Nash
agents who make time-varying production and distribution decisions
relative to the same single homogenous output. The Cournot-Nash
followers are described by the dynamic oligopolistic network
competition model developed in Friesz et al (2003) and which takes the
form of a differential variational inequality. We show that this
differential variational inequality is equivalent to a functional
nonlinear complementarity problem (NCP). Consequently, the complete
Stackelberg game/bilevel model takes the form of a continuous time
optimal control problem constrained by a functional nonlinear
complementarity problem (FNCP).
The specific decision environment we consider is one for which the
followers are in Cournot-Nash oligopolistic game theoretic competition
according to dynamics that describe the trajectories of inventories and
correspond to flow conservation for each firm at each node of the
network of interest. The market for physical distribution (delivery)
services is at the origin-destination level and is perfectly
competitive. That is, the oligopolistic firms, acting as shippers,
compete as price takers in the market for physical distribution
services. Joining them in this competition for physical distribution
services are other very numerous agents who employ the same
distribution services to support their activities in markets distinct
from that for our homogeneous commodity of interest. Although the
oligopolistic producers cannot influence physical distribution prices,
they can consider and react to temporal fluctuations in distribution
service pricing and to congestion arising in the distribution network.
The complete, bilevel Stackelberg determines plant-specific outputs,
the allocations of outputs to meet demands and output shipment patterns
(all three of which are control variables), as well as output
inventories (which are state variables) when there firms compete by
constructing temporal as well as spatial strategies for their network
activities. We discuss alternative philosophies for solving optimal
control problems with FNCP constraints. We provide the details of a
discrete time, finite dimensional mathematical programming
approximation. We present a numerical example based on this
approximation.
Title:
Dynamic flow management of on-demand service and production networks
Author:
Costis Maglaras, Columbia University,
c.maglaras[at]columbia.edu
This talk overviews some recent results on problems of revenue
management and control of stochastic service and production networks.
Such problems combine tactical economic decisions of dynamic pricing and
capacity control, with operational decisions of order sequencing,
prioritization, and leadtime quotation. Focusing on a relatively simple
system that is capabale of producing multiple products, we will present
some structural results about the degree in which the various types of
control interact with each other, their respective time-scales, and
their impact on the overall system profitability. Most of these insights
remain valid in more complex service and production systems.
Title:
Token-Bucket Pricing for Shared Services
Authors: Opher Baron, Rotman School of Management, University of
Toronto
Opher.Baron[at]Rotman.Utoronto.Ca
Dirk Beyer, HP Labs, Palo Alto, CA
dirk_beyer[at]hp.com
Gabriel Bitran, Sloan School of Management, Massachusetts Institute of
Technology
gbitran[at]mit.edu
This paper presents novel Token-Bucket pricing-schemes for
shared-services and a framework for their analysis. One of the major
barriers for implementing these schemes is the buyer's challenge of
choosing token buckets' parameters. The buyer'sobjective is to minimize
expenditures such that the probability of shortages in any period, as
dictated by token-bucket admission controls is lower than a threshold.
We show that the event of a loss (backlog) in this system is equivalent
to the event of a two-(one-) sided regulated-random-walk to reach
(cross) a threshold. The buyer's control parameters are the token rate
(drift of the random-walk) and the bucket depth (threshold to be
crossed). Using the theory of large deviations, we bound the
probability that a two-sided regulated-random-walk reaches a threshold
and give convex approximations for the buyer's problem. Therefore,
under mild assumptions, the buyer's controls can be efficiently
approximated. Furthermore, tailored closed-form solutions and
approximations for cases of exponential and normal demands are
provided. We show empirically that for normal demand the costs of these
approximations are typically within 2% of the optimal ones for service
levels higher than 80%. The results of this paper support the use of
token bucket pricing schemes for shared services.
Title: Modeling extensions for a class of
business-to-business revenue management problems
Author:
Nicola Secomandi, Graduate School of Industrial Administration,
Carnegie Mellon University
ns7[at]andrew.cmu.edu
Recently, the business consulting and operations research communities
have shown great interest in porting revenue management (RM) principles
from traditional business-to- consumer (B2C) settings to the realm of
business-to-business (B2B) e-commerce. The talk discusses how existing
B2C RM inventory and pricing control models can be unified and extended
to deal with a class of B2B RM problems.
Title: Dynamic Cross-Selling in E-commerce
Retailing
Authors: Serguei Netessine, The Wharton School, University of
Pennsylvania
netessin[at]wharton.upenn.edu
Sergei Savin, Graduate School of Business, Columbia University
svs30[at]columbia.edu
Wen-Qiang Xiao, Graduate School of Business, Columbia University
WXiao09[at]gsb.columbia.edu
We consider the problem of dynamically cross-selling products (e.g.,
books) or services (e.g., travel reservations) in e-commerce setting.
In particular, a company faces a stream of stochastic customer arrivals
and may offer, in addition to the product desired by the customer, a
package of this product with another one. We formulate the
cross-selling problem as a stochastic dynamic program blended with
combinatorial optimization. Given consumer preferences and product
inventory, we analyze two related issues: (1) how to dynamically select
products to cross-sell and (2) how to dynamically price these product
packages.
Title: A Robust Nonlinear Fluid Model of Dynamic
Pricing and Inventory Control with no Backorders.
Authors: E. Adida and G. Perakis, MIT
eadida[at]MIT.EDU
georgiap[at]MIT.EDU
We study a fluid model for dynamic pricing and inventory control in a
make-to-stock system. First demand is deterministic and subsequently
demand is uncertain. We consider a multi-product capacitated, dynamic
setting with no backorders. We introduce an algorithm that computes the
optimal production and pricing policy on a finite time horizon. When
demand is uncertain we consider the additive demand case. We use ideas
from robust optimization. We give numerical examples and discuss some
insights.
Title: Data-driven revenue management
Authors: Dimitris Bertsimas and Aurelie Thiele, MIT
dbertsim[at]mit.edu
aurelie[at]mit.edu
We present an approach on revenue management problems under uncertainty
that directly works with the data, and is based on the belief that what
is the primitive in models of practical relevance is data, not
distributions. The method leads to insightful solutions, and promising
empirical behavior.
Title: Multiperiod Pricing for Perishable Products;
a Robust Optimization Approach
Authors: Anshul Sood, (Presenter), and G. Perakis, MIT
anshul[at]MIT.EDU
georgiap[at]MIT.EDU
We propose a model for pricing a single perishable product in a
multi-period oligopolistic market with many sellers competing through
pricing. We address the competitive aspect of the problem together with
demand uncertainty. In particular, we propose a model that uses ideas
from robust optimization. We establish existence and uniqueness of
pricing equilibrium policies and introduce an iterative learning
algorithm for computing the equilibrium policy. We illustrate our
results through some numerical examples and discuss some insights.
Title: Online Low Price Guarantees - A Real
Options Analysis
Authors: Chris K. Anderson, Ivey School of Business, University of
Western Ontario
Ben Marcus, Ivey School of Business, University of Western Ontario
Montgomery Blair, Dollar Thrifty Automotive Group
A common practice among large retailers is the low price guarantee,
rebating consumers if they find an identical product cheaper elsewhere,
or if the retailer discounts the good within a specified timeframe.
This provides consumers with some level of comfort in their purchase
decision. A similar low price guarantee is provided by numerous service
industries that allow reservation of capacity yet don't penalize the
consumer for failure to keep that reservation; examples include hotels
and car rental. Given that a consumer is not required to keep the
reservation, they may make another reservation either at a competing
firm or the same firm if future prices decline (a similar dilution of
reservation occurs with corporate clients whom are allowed to also pay
regular rates). The increasing availability of pricing information on
the Internet affords consumers the opportunity to be more strategic in
their behaviors. As a consumer we are able to quickly and easily check
prices from numerous service or goods providers. The ease of price
information potentially makes these guarantees very costly to the
service or good provider. We analyze the implied costs associated with
these guarantees by making analogies to financial options. Motivation
for this research comes from a large car rental firm, Dollar Thrifty
Automotive Group Inc., which is currently considering offering a low
price guarantee to all consumers whom book a reservation though their
website.
Title: Analysis of a Yield Management Model for On
Demand IT Services
Authors:
Yezekael Hayel, INRIA-IRISA, Rennes, France,
yhayel[at]irisa.fr
Laura Wynter, IBM Research, Hawthorne, NY,
lwynter[at]us.ibm.com
Parijat Dube, IBM Research, Hawthorne, NY,
pdube[at]us.ibm.com
In [WLD03], a model for performing yield management in the context of
IT provisioning was presented. This model is especially valuable in the
context of an on demand operating environment. On demand IT services
allow users to arrive at will into an IT system, in which scheduled
jobs have already reserved some of the resources. Many potential
applications of this type of IT infrastructure exist and a few are
already in operation.
One example of an on demand IT service that exists today is the case of
dynamic off-loading of web content. When a customer, such as an online
retailer, experiences very heavy web site traffic, that retailer may
have its excess traffic automatically redirected to an off-loading
service. The process is invisible to end-users of the retailer. The
yield management model described in [WLD03] could be used by the
off-loading service provider to allocate its own capacity optimally and
profitably. Many other potential applications of this technology are on
the horizon: application service providers may run software
applications on their own cluster of servers and allow customers, for a
fee, to use those applications remotely. Yield management in this case
sets capacity allocations (server use, storage, and bandwidth) and
multiple price points to offer to those customers, depending on the
available resource level of the service provider, as well as the market
demand. Similarly, computing centers, that rent processing capacity to
customers, can operate more profitably and more efficiently through use
of a system of yield management.
In this presentation, we present a detailed analysis of that model,
both in simplified cases where an analytical analysis is possible, and
numerically on larger problem instances, and confirm the significant
revenue benefit that can accrue through use of yield management in an
IT on demand operating environment.
Title: Equilibrium problems with equilibrium
constraints: A new modeling paradigm for revenue management
Authors: Houyuan Jiang, Daniel Ralph, Stefan Scholtes, The Judge
Institute of Management, University of Cambridge
h.jiang[at]jims.cam.ac.uk
d.ralph[at]jims.cam.ac.uk
s.scholtes[at]jims.cam.ac.uk
Pricing and seat allocation are two classical problems in revenue
management. Traditionally, they are studied separately. Recently, some
researchers started to consider pricing and seat allocation in an
integrated manner in order to achieve higher revenue. At the same time,
revenue management under competition has received considerable
attention recently. In this paper, we use a framework of equilibrium
problems with equilibrium constraints (EPEC) to model both competition
between airlines and the integration of pricing and seat allocation for
each airline. We review the literature of EPEC and discuss existence
and uniqueness issues. We also discuss numerical approaches for solving
EPEC models arising from airline revenue management.
Title: Revenue Management Through Cross-Selling
Authors: E. Lerzan Ormeci, Department of Industrial Engineering, Koc
University
lormeci[at]ku.edu.tr
O. Zeynep Aksin ,Graduate School of Business, Koc University
ZAKSIN[at]ku.edu.tr
Evrim-Didem Gunes, INSEAD, Boulevard de Constance
evrim-didem.gunes[at]insead.edu
Many firms in mature industries resort to growth by deepening customer
relationships and making them more profitable rather than increasing
market share. A significant part of this profitability comes from
revenues generated by the sale of additional products and services to
existing customers. Given the growing dislike among consumers for
telemarketing, this type of selling is increasingly being performed via
cross-selling and up-selling initiatives. Inbound call centers are an
important point of contact with the customer, where this type of
selling takes place. We model the cross-selling problem of a call
center as a dynamic admission control problem to address the question
of when and to whom to cross-sell such that revenue generation is
maximized while congestion costs are kept as low as possible. Optimal
dynamic cross-selling policies are partially characterized, and certain
structural properties are explored. We relate our findings to standard
marketing practice in such problems.
Title: Optimal Dynamic Stochastic Network Yield
Management
Author: Xiubin Wang, Transportation and Logistics Research Center,
University of Wisconsin-Superior
xwang1[at]uwsuper.edu
We discuss the optimal solution to the general dynamic stochastic
network yield management in the context of airline operation. Necessary
and sufficient conditions are identified for a policy change. A
solution scheme is proposed to find the pivotal times for each fare
bucket prior to departure. It is a double recursion from the end to
start of the sales season and from low capacity to high capacity. The
solution presented is general.
Title: Efficiency Loss in Resource Allocation
and Supplier Selection Games
Authors: John Tsitsiklis, Ramesh Johari, MIT
jnt[at]mit.edu
rjohari[at]mit.edu
Motivated by certain proposals for network resource allocation, we
analyze a simple game in which the users of congested finite-capacity
links anticipate the effect of their actions on the link prices. We
discuss existence and uniqueness of a Nash equilibrium, and establish
that the efficiency of the system drops by no more than 25% relative to
the social optimum. We extend the results to a more general resource
allocation mechanism in which a set of producers compete for a limited
amount of available resources. Motivated by electricity markets, we
consider an analogous situation involving a set of competing suppliers
who bid in order to satisfy a prespecified demand. We show that if each
producer's "bid" consists of a supply function within a certain
one-parameter family, the efficiency loss at a resulting Nash
equilibrium can be bounded and decreases to zero with the number of
suppliers. Finally, we argue that the particular families of demand and
supply functions we consider are the only ones that possess certain
desirable properties.
Title: Hotel Revenue Management in Competitive Market
Author: Hong Jin, Starwood Hotels and Resorts Worldwide, Inc.
jin[at]alum.mit.edu
This talk addresses the key issues in hotel Revenue Management (RM) in
competitive markets, including competitive pricing, channel management,
inventory control, market response analysis and etc. The paper start
with discuss the key planning and operational issues in hotel RM. Then,
it presents a new analytical RM approach developed and applied for a
big hotel in a competitive metropolitan area, with comparison to the
traditional RM approach currently adopted by the most hotels. To
illustrate the contributions of the new approach, the paper shows the
improvement results in the key performance metrics, highlighted by a
40% growth in both RevPAR index and total revenues year over year. This
analysis is a combination of theory/modeling discussion and operational
experiences and best practices in applying the new RM approach in a
competitive market. The key contribution of the new approach is making
complex optimization/statistics based analytical approach highly
operational in effectively managing hotel daily pricing and inventory.
The new approach provides some new techniques in solving some key RM
issues haunting hotel revenue managers for a long time, e.g.
incorporating competitive pricing into RM/Pricing and Inventory
Control.
Title: Business Rule Constrained Price Discovery
Models in Transportation Procurement Auctions
Authors: Jiongjiong Song, Institute of Transportation Studies,
University of California
jiongjis[at]uci.edu
Amelia Regan, Graduate School of Management, University of California
aregan[at]uci.edu
B2B auctions are a dominant price discovery mechanism for
transportation service procurement in shipper’s logistics operations.
However, shippers are struggling with decisions on how to assign bids
to carriers considering both costs and business requirements. In this
work, we propose optimization based bid analysis models and examine
heuristic algorithms with numerical analysis.
Title: Optimal Price Design for Variable Capacity
Outsourcing Contracts
Authors: Chris Kenyon and Giuseppe Paleologo, IBM Research (ZRL, WRL)
chk[at]zurich.ibm.com
gappy[at]us.ibm.com
Outsourcing of information technology (IT) infrastructure and business
processes (BP) is a significant aspect of the business landscape with
contract signings in the tens of billions each year for the major
providers (e.g. Accenture, EDS, IBM). A recent trend emphasizes
variable capacity contracts linked to business or IT metrics for medium
to long term (5-10 year) deals. This new emphasis on variability is
linked to a growth in interest in the accompanying pricing schemes. A
basic tension in the design of these pricing schemes occurs between the
objectives of the outsourcing provider (hereafter Provider) and the
company whose IT or BP are being outsourced (hereafter Company). In
other words their utility functions on contract attributes differ. For
example the Company may desire utility-style pricing whilst the
Provider may be interested in the certainty with which it achieves a
given margin. The pricing design tension is accentuated by:
history-dependent capacity costs for many contract elements to the
Provider; the wide range of capacities of interest to the Company; and
the unpredictability of actual events over the contract lifetime.
Contract elements typically include dependencies on people, hardware,
and software licenses. These dependencies introduce significant
delivery cost granularity, dependence on the direction of capacity
change, potential delivery lags, and cost non-linearities. In this
paper we characterize the price design problem for variable capacity
outsourcing contracts and provide a multi-objective utility theory
framework for obtaining optimal solutions. Furthermore we show how to
construct a pareto-efficient frontier of price designs with axes of
Provider-utility and Company-utility by systematically varying contract
design parameters. This frontier serves as an appropriate space for
contract negotiation.
Title: Dynamic Pricing under Strategic Consumption
Authors: Chunyang Tong and Sriram Dasu, Marshall School of Business,
University of Southern California
dasu[at]marshall.usc.edu
We investigate the dynamic pricing of limited capacity when the seller
faces strategic buyers, who make their purchase decision based on the
currently prevailing price as well as their anticipation of the future
price to be posted by the seller. Two pricing schemes are analyzed:
upfront pricing scheme in which the seller posts his pricing path at
the start of the selling horizon and buyers choose the time for their
bidding offer; contingent pricing scheme under which the seller
determines the price based on the realized sale. For the above two
pricing scheme, consumers’ equilibrium behavior is characterized and
optimal pricing-path for the seller is analyzed. Numerical studies are
conducted to show the potential gains for the seller if he considers
buyer’s strategic behavior. We conclude that buyer’s strategic behavior
plays an important role in seller’s optimal pricing scheme and his
profitability
Title: An Asymptotically-Optimal Dynamic Admission
Policy for a Revenue Management Problem
Authors: Marty Reiman, and Qiong Wang, Bell Labs
marty[at]research.bell-labs.com
qwang[at]research.bell-labs.com
We consider the following canonical revenue management problem, which
has been analyzed in the context of airline seat inventory control and
has applications to other service industries and supply chain
management. There are M resource types (legs) and J customer classes
(routes). There is a total of Cm of resource m (seats on leg m),
m=1,…,M. Customers of class j arrive in a (possibly nonstationary)
stochastic process, require Amj units of resource m, and pay pj if
accepted. The aim is to make dynamic accept/reject decisions to
maximize the total expected revenue obtained over the finite horizon
[0,T] subject to the resource constraints Cm. We introduce a control
policy motivated by fluid and diffusion limits (as the Cm’s and arrival
rates grow large). Our control policy makes an initial resource
allocation decision based on solving a linear program (LP). This leads
to a determination of which classes to accept and which to reject. The
LP is re-solved (using the remaining capacities) at successive ‘trigger
points’ until the end of the finite horizon at T. Our policy preserves
desired features of existing approaches, such as nesting, bid-price
control, and adaptive optimization, while avoiding known potential
pitfalls associated with these schemes. We provide both a theoretical
justification as well as numerical comparisons with existing schemes to
validate our approach.
Title: Price-Directed Control of a Closed
Logistics Queueing Network
Author: Dan Adelman, Graduate School of Business, The University of
Chicago
dan.adelman[at]gsb.uchicago.edu
Motivated by one of the leading intermodal logistics suppliers in the
United States, we consider an internal pricing mechanism for managing a
fleet of service units (shipping containers) flowing in a closed
queueing network. Nodes represent geographic locations
and arcs represent travel between them. Customer requests for arcs
arrive over time, and the problem is to find an accept/reject policy
that maximizes the long-run time average reward rate from accepting
requests.
We formulate the problem as a semi-Markov decision process, and give a
simple linear program that provides an upper bound on the optimal reward
rate. Using Palm calculus, we derive a nonlinear program that
approximately captures queueing and stockout effects on the network. We
show that this nonlinear program provides a new, alternative derivation
of an upper bound due to Harel (1988) for the Erlang loss probability.
We also interpret the optimality conditions as optimizing the behaviour
of decentralized agents assigned to units. Using optimal solution
information in an auxiliary pricing problem, we construct an easily
computable functional approximation to the dynamic programming value
function. The resulting policy is computationally efficient and produces
superior economic performance as compared with other policies and the
upper bound. Furthermore, it provides a methodologically grounded
solution to the firm's internal pricing problem.
Title: Pricing & Elasticity Price on segmented
markets under a Conjoint Study
Author: Agustín Cano, Aeromexico
acano[at]aeromexico.com.mx
The objective is finding and understanding the market response to
different critical variables in mexican airline markets. Therefore an
airline made a marketing study using the concept of Choice Based
Conjoint that helped understand the elasticity of the market, and how
the Brand can support price differentiation with competitors. The
conclusion of this study will be shown.
Title: An MPEC Approach to Dynamic Pricing and
Demand Learning
Authors: Soulaymane Kachani (Columbia University, IEOR Department)
sk2267[at]columbia.edu
Georgia Perakis (MIT, Sloan School)
georgiap[at]MIT.EDU
Carine Simon (MIT, Operations Research Center)
casimon[at]mit.edu
In this talk, we present an optimization framework that addresses joint
pricing and demand learning in an oligopoly setting in order to
maximize expected revenue for perishable products under fixed capacity
constraints. The model we consider assumes a parametric family of a
company’s demand as a function of its price and other competitors’
prices, the parameters of which are dynamic and are learned over time.
We show how the problem reduces to a mathematical program with
equilibrium constraints (MPEC) and study the properties of the model.
We propose two solution methods. One of these methods solves the lower
level using a fictitious play scheme. We illustrate our approach using
a 3-company example.
Title: Algorithms for Revenue Management in
Unrestricted Fare Markets
Authors: Craig Hopperstad, Hopperstad Consulting
cah[at]gte.net
Peter Belobaba, MIT International Center for Air Transportation
belobaba[at]mit.edu
While the assumption of independent fare class demands was at best
questionable even under traditional restricted fare structures, it is
completely invalid under the increasingly less restricted fare
structures typical of low-fare airline markets. We present some
alternative approaches to RM seat inventory control of unrestricted
fare classes, including adjustments to EMSRb methods to account for
sell-up, willingness-to-pay models, and dynamic programming techniques.
We use the Passenger Origin-Destination Simulation (PODS) to assess the
performance of these RM methods in a competitive airline market
environment.
Title: Dynamic Pricing of Information Goods under
Demand Uncertainty
Author: Eric W. Cope, The Sauder School of Business, University of
British Columbia
eric.cope[at]sauder.ubc.ca
E-business retailers have used dynamic pricing as a means of estimating
aggregate customer demand response to price changes when this response
is initially unknown. This study looks at dynamic pricing in the
context of learning customer demand for information goods sold on the
Internet. Because information goods can be replicated quickly at almost
no cost, issues with inventory and production generally do not arise.
Rather, the major concern in designing a dynamic pricing strategy in
this setting is efficiently managing the exploration-exploitation
trade-offs involved in price selection. We introduce a nonparametric
Bayesian model of demand uncertainty involving Dirichlet priors, which
are very flexible and easy to elicit, and encode many natural
assumptions about the dependence among various demand values. We
provide both analytic formulas and efficient approximation methods for
computing the posterior distributions after sales data have been
observed. Within the framework of this Bayesian model, we investigate
several "index function" heuristics for sequential pricing. These
heuristics are shown to be efficient in terms of both performance and
computation, and they are robust to misspecifications of the prior.
Finally, we show how to extend these methods to dynamically price
multiple "versioned" or "bundled" information goods.
Title: Problems of Contract Pricing
Authors: Ahmet Kuyumcu and Dinesh Mehta, Zilliant, Inc,
ahmet.kuyumcu[at]zilliant.com
dinesh.mehta[at]zilliant.com
Most business transactions are governed by contractual agreements.
Companies (buyers, sellers, or both) may commit to terms and conditions
of contracts that may be in effect for many years. Profitable contract
management involves optimizing these terms and conditions and requires
solving different forms of pricing problems such as list pricing, floor
pricing, spot pricing, and rebate/incentive optimization. This
presentation defines pricing models that arise from contractual
agreements and focuses on a specific case study.
Title: Dynamic Pricing for Parallel Flights
Authors: Dan Zhang, Graduate Program in Industrial Engineering,
University of Minnesota
William L. Cooper, Graduate Program in Industrial Engineering,
University of Minnesota
billcoop[at]me.umn.edu
We describe a Markov decision process model for dynamically pricing
multiple flights between a common origin and destination. Consumers
decide what, if anything, to purchase based upon the prices of all the
flights as well as their own preferences. We describe analytical
properties of the problem as well as computational techniques. The work
is motivated by a situation faced by low-fare carriers who sell tickets
on their own websites over the Internet.
Title: A Value Function Decomposition Approach to
Revenue Management Network Problems
Author: Darius Walczak, PROS Revenue Management,
dwalczak[at]prosrm.com
We show an intuitive way to decompose value function of a popular
dynamic network problem and how to apply this decomposition to generate
approximate dynamic solutions fast. Network problems arise naturally in
many RM and Pricing applications with the airline Origin and
Destination network (of flights) being a well-known example. While in
principle these problems can be solved optimally by recursion,
straightforward approaches suffer from the 'curse of dimensionality'
and thus are not feasible to implement. We present numerical results
for a small (but relevant) network with products that can be offered at
a number of price points.
Title: Optimal Pricing and Replenishment in a
Single-Product Inventory System
Authors: Hong Chen, Cheung Kong Graduate School of Business
Owen Wu, Sauder School of Business, University of British Columbia,
owen.wu[at]sauder.ubc.ca
David D. Yao, Columbia University
We study an inventory system that supplies price-sensitive demand
modeled by Brownian motion, focusing on the optimal pricing and
inventory replenishment decisions, under both long-run average and
discounted objectives. Analytical solutions are obtained in all cases,
and related to or contrasted against previously known results wherever
applicable. In addition, we bring out the interplay between the pricing
and the replenishment decisions, and the way they react to demand
uncertainty. We show that the joint optimization of both decisions
results in significant profit improvement over the traditional way of
making the decisions separately or sequentially. We also show that
changing price over time will only result in a limited profit
improvement over a single price (when both are optimally determined).
The relative improvement becomes more significant in applications where
the profit margin is low. An analytical bound on the profit improvement
is derived.
Title: Incentive-Compatible Revenue Management in
Queueing Systems: Optimal Strategic Idleness and other Delaying Tactics
Author: Philipp Afeche, Kellogg School of Management,
p-afeche[at]kellogg.northwestern.edu
How should a capacity-constrained firm design an incentive-compatible
price-scheduling mechanism to maximize revenues from a heterogeneous
pool of time-sensitive customers with private information on their
willingness to pay, time-sensitivity and processing requirement? I
consider this question in the context of a queueing system that serves
two customer types and provide the following insights. First, delay
cost-minimization, which plays a prominent role in controlling and
pricing queueing systems, should in this setting not be the only
criterion ex ante. Second, this problem gives rise to optimal
scheduling policies with novel features. Compared to the delay
cost-minimizing scheduling policy, these policies increase, decrease or
reverse the delay differentiation between customer types. The optimal
level of delay differentiation emerges from a trade-off between
operational constraints and customer incentives. The approach presented
in this talk can be adapted for designing revenue-maximizing and
incentive-compatible mechanisms in systems with different properties.
Title: Capacity Allocation to Support Customer
Segmentation by Product Preference
Authors: Guillermo Gallego, Columbia University
Ozalp Ozer, Stanford University
Robert Phillips, Nomis Solutions
A capacity-constrained supplier sells into a market with exogenous
prices and customers segmented by product preferences. What product
should the supplier offer to buyers from each segment as they arrive?
How should this decision be updated? This problem is relevant both to
traditional revenue management industries such as hotels and cruise
lines which offer products of different qualities as well as to
contract manufacturers who may differentiate customers by lead time. We
present both structural results and numerical simulations.
Title: Optimizing Decisions over the Long-term in
the Presence of Uncertain Response
Author: Edward Kambour, PROS Revenue Management,
ekambour[at]prosrm.com
When there is random response to discrete actions, one can utilize
expected returns as the objective. This process is complicated when the
underlying parameters of the response are not known. In such cases the
only way to decrease the uncertainty and become more confident in
future decisions is gather information over the decision space.
However, this process does not necessarily coincide with that which
maximizes expected return in the short run. In this discussion we will
examine, in a rigorous mathematical fashion, the effects of uncertainty
on the general decision-making process. The goal is to balance the
value of gathering additional data versus the implementation of the
action that (as of now) has the highest expected return.
Title: Dynamic Revenue Management Games
with Forward and Spot Markets
Authors: Guillermo Gallego, Columbia University
Srinivas Krishnamoorthy, Columbia University
sk711[at]columbia.edu
Robert Phillips, Columbia University
We consider a market with two capacity providers – a low cost entrant
and an established incumbent - each with fixed capacity, who compete to
sell in a spot market and a forward market. Prices are fixed and the
providers make strategic capacity allocation decisions. We identify the
type of equilibrium behavior we could expect from the two providers
under different assumptions about market structure, demand and
available capacity. For the infinitely repeated game, we identify the
existence of subgame-perfect Nash equilibria and show how the two
providers can obtain higher average revenues (than static Nash
equilibrium revenues) by implicit collusion. The study has implications
for revenue management markets where providers have dynamic competitive
interactions rather then a single static interaction.
Title: Price and service competition among service
providers operating general queueing facilities
Authors: G.Allon and A.Federgruen, Columbia University
We analyze a general market for an industry of competing service
facilities. Other than through intrinsic and unalterable service
characteristics, firms differentiate themselves in terms of their price
levels and the waiting time customers experience. The expected market
share a firm acquires may thus depend on all firms' prices and their
stated guarantees (service levels) for the expected waiting time or a
given fractile thereof. The equilibrium behavior under price and/ or
service competition depends critically on the structure of the capacity
costs incurred to meet a given service level under a given demand
volume. We show that a large variety of queuing models give rise to a
specific class of parameterized capacity cost functions. We
characterize the impact of the structure of these capacity cost
functions on the equilibrium behavior in the industry, under various
types of competition.
Title: Estimating the Impact of Revenue
Management Decisions
Author: Warren Lieberman, Veritec Solutions
warren[at]veritecsolutions.com
While it has always been important, estimating the financial impacts of
revenue management is receiving increasingly greater attention as
revenue management extends its reach to more and more industries. In
this presentation, we explore a new methodology for estimating the
incremental financial contribution from revenue management decisions.
Interestingly, the concepts underlying this methodology may also extend
to providing a new basis for revenue management decision-making in some
industries.
Title: Dynamic Negotiation Guidelines for
Make-To-Order Contracts
Authors: Jeremie Gallien, Yann Le Tallec and Tor Schoenmeyr MIT Sloan
School of Management and Operations Research Center
jgallien[at]mit.edu
We seek to identify quantitative, dynamic guidelines for the sales
negotiations of make-to-order contracts along the dimensions of price,
quantity and delivery lead-time. We study accordingly the dynamic
programming formulation of a related admission control problem, and
derive a dynamic, computationally efficient policy using a fluid
approximation. Simulation experiments reveal that its performance is
superior to that of static policies over a important range of market
environments. In addition, a comparison with the optimal anticipative
policy allows to quantify the value of better forecasting. Finally, we
discuss the potential implementation of this work into a decision
support system.
Title: Auction Algorithms for Market Equilibrium
Authors: Rahul Garg and Sanjiv Kapoor.
grahul[at]in.ibm.com
kapoor[at]iit.edu
In this paper we study algorithms for computing market equilibrium in
markets with linear utility functions. The buyers in the market have an
initial endowment given by a portfolio of items. The market equilibrium
problem is to compute a price vector which ensures market clearing,
i.e. the demand of a good equals its supply, and given the prices, each
buyer maximizes his utility. The problem is of considerable interest in
Economics. This paper presents a formulation of the market equilibrium
problem as a non-linear program. We construct the dual of this
non-linear formulation and define conditions under which prices achieve
market clearing. These conditions arise naturally from complementary
slackness conditions. We then define an auction mechanism which
computes prices such that approximate market clearing is achieved. The
algorithm we obtain outperforms previously known methods.
Title: On the Choice-Based LP Model for Network RM
Author: Garrett van Ryzin
and Qian Liu,
Columbia University
Recently, Gallego, Iyunger and Phillips proposed a choice-based LP
model for network revenue management which parallels the deterministic
linear programming model widely used in current network RM. We build on
their work in two ways: First, we characterize the offer sets (sets of
available fare products) produced by this model by extending a notion
of "efficiency" proposed by Talluri and van Ryzin and show that,
asymptotically as demand and capacity are scaled up, only these
efficient sets are used in an optimal policy. Second, we propose a
practical decomposition heuristic that significantly improves on the
performance of the static LP solution. We illustrate the method on
several numerical examples and discuss various extensions.
Plenary Talks
Title: How the Internet is Impacting Revenue
Management, Pricing, and Distribution
Speaker: E. Andrew Boyd
Chief Scientist and Senior Vice President of Science and Research
PROS Revenue Management
The exuberance of investors toward Internet companies in the late
1990’s stemmed from a realization that the Internet was destined to
have an enormous impact on business. While most Internet start-ups no
longer exist, their vision of how the Internet would change our lives
is being realized at a pace even they might not have imagined.
Revenue management and dynamic pricing in an e-commerce setting
pre-date the Internet by at least two decades, as witnessed by
historical developments in the travel and transportation industries.
Yet, characteristics unique to the Internet are bringing about
tremendous changes to distribution and therefore to the mathematical
foundations of revenue management and dynamic pricing. We discuss some
of the changes being brought about by the Internet and the research
challenges they imply for the near future.
Title: Callable and Flexible Revenue Management
Speaker: Guillermo Gallego
(Columbia University
)
Joint work with Garud Iyengar, Steven Kou
(Columbia University)
and Robert Phillips (Stanford University)
Revenue management is utilized in industries where a low-to-high fare
booking or sales process culminates with the delivery of a good or a
service. The low to high fare structure requires a careful allocation
of capacity to balance the cost of high-fare lost sales and unused
capacity. Capacity providers can increase profits, service their
customers better and improve capacity utilization if they can implement
a mechanism to broker the transfer of capacity from low to high
valuation customers. In this talk we investigate mechanisms to induce
low fare customers to self-select and grant the capacity provider the
option to recall their capacity. We discuss both the single-leg model
where cash is the inducement and the network model where the inducement
is a combination of cash and an alternative product.