Informs 2015

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INFORMS Philadelphia – 2015
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4 - Analysis of Competitive Pricing with Multiple Overlapping
Competing Bids in Revenue Management
Goutam Dutta, Professor, Indian Institute of Management, Wing
3, PMQ Area, Roon No 3H, Old Campus, Ahmedabad, 380015,
, Sumeetha Natesan
We analyze the competitive pricing situation of one company with more than one
competitor. Based on the past experience one can have some idea about what the
competitors will bid which can be described by various distributions. The prices of
company and its competitors follow independent, overlapping uniform
distribution. First we derive expression for probability of win and then, we
attempt to derive conditions for maximizing the expected contribution to profit.
5 - Data Science, Operations Research, Analytics and
Revenue Management
Jon Higbie, Managing Partner & Chief Scientist, Revenue
Analytics, 3100 Cumberland Blvd., Suite 1000, Atlanta, GA,
30339, United States of America,
The term Data Science and the job title Data Scientist are much in vogue right
now. How does Data Science relate to Operations Research? What lessons might
we learn about the past boom in Operations Research that can be applied to the
current boom in Analytics? How is the increased emphasis on Business Analytics
impacting Revenue Management? These questions will be explored through a
series of case studies, and discussion.
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44-Room 103B, CC
Algorithmic Revenue Management with
Strategic Customers
Sponsor: Revenue Management and Pricing
Sponsored Session
Chair: Vivek Farias, Associate Professor, MIT, 100 Main Street,
Cambridge, United States of America,
Co-Chair: Yiwei Chen,
1 - Analysis of Discrete Choice Models: A Welfare-Based Framework
Guiyun Feng, University of Minnesota, 1006 27th Avenue SE,,
Minneapolis, MN, 55414, United States of America,
, Zizhuo Wang, Xiaobo Li
We propose a framework for discrete choice models through a welfare function.
The framework provides a new way of constructing choice models. It also
provides great analysis convenience for establishing connections among existing
choice models. We define a new property in choice models:
substitutability/complementarity and study conditions for a choice model to be
substitutable. We show that our framework is flexible in this property, which is
desirable in capturing practical choice patterns.
2 - Robust Dynamic Pricing with Strategic Customers
Yiwei Chen, Assistant Professor, Renmin University of China, No.
59 Zhongguancun Street, Beijing, China, ,
Vivek Farias
We consider the canonical revenue management problem that a seller sells finite
number of a product over a finite horizon via dynamic pricing. We assume that
customers are forward looking with heterogeneous strategic factors (time
discount rates and monitoring costs). We propose a class of pricing policies that
achieve at least 29% of revenue under an optimal dynamic mechanism. Our
policies require no knowledge of customers’ strategic factors and ensure
customers to behave myopically.
3 - Managing Multi-period Production Systems with Limited
Process Flexibility
Yehua Wei, Fuqua School of Business, Duke University, 100
Fuqua Drive, Durham, NC, 27708, United States of America,
Cong Shi, Yuan Zhong
We develop a theory for the design of process flexibility in a multi-period
production system. We propose and formalize a notion of “effective chaining”
termed the Generalized Chaining Condition (GCC). We show that any partial
flexibility structure that satisfies GCC is near-optimal under a class of policies
called the Max-Weight policies. Furthermore, we show that GCC can be satisfied
using just k arcs, where k is the equal to the number of products plus the number
of plants.
4 - Optimal Dynamic Pricing with Patient Customers
Yan Liu, University of Minnesota, Room L123, ME Building, 111
Church ST, Minneapolis, MN, 55455, United States of America,
William Cooper
We consider a single-product pricing problem in which a fraction of customers is
patient and the remaining fraction is myopic. A patient customer will wait up to
some fixed number of time periods for the price to fall below his valuation, at
which point the customer will make a purchase. If the price does not fall below a
patient customer’s valuation at any time during those periods, then the customer
will leave without buying. We identify the structure of an optimal pricing policy.
SD45
45-Room 103C, CC
Pricing — Examples of Collaboration Between
Academia and Industry
Sponsor: Revenue Management and Pricing
Sponsored Session
Chair: Georgia Perakis, MIT, 77 Massachusetts Avenue, Cambridge,
MA, 02139, United States of America,
1 - Balancing Profit Maximization and Inventory for Recommending
Personalized Bundles
Anna Papush, MIT, 77 Massachusetts Avenue, Cambridge, MA,
United States of America, Georgia Perakis,
Pavithra Harsha
Market forecasts show that e-commerce stands to ultimately inherit a significant
proportion of the retail market. Gaining a competitive edge in this sector is of
utmost importance to any firm’s success. The model presented in this work
guarantees customer satisfaction by providing relevant recommendations at
personalized prices in a way that balances profit maximization with business
operations. We demonstrate its value on actual e-tailer data.
2 - Pricing for a Satellite Service Provider
Charles Thraves, MIT, 77 Massachusetts Avenue, Cambridge, MA,
02139, United States of America, ,
Georgia Perakis
We present a pricing optimization formulation of the data plans of a satellite
service provider. First, to estimate reservation prices for all customers (including
unobserved customers) we deal with the missing data problem. We introduce a
MIP formulation and develop properties and heuristics for the problem. We
develop analytical bounds for our heuristics and conclude that they can help the
company increase its profits by more than 10%.
3 - Scheduling Promotion Vehicles to Boost Profits
Lennart Baardman, MIT, Operations Research Center, Cambridge,
MA, 02139, United States of America, Kiran
Panchamgam, Danny Segev, Georgia Perakis, Maxime Cohen
Retailers use promotion vehicles (e.g. flyers, commercials) to increase profits. We
model how to assign promotion vehicles to maximize profits as an NLIP. The
problem is NP-hard and even hard to approximate. However, we construct an
epsilon-approximation in the form of an IP of polynomial size. Also, we propose a
greedy algorithm with a provable guarantee and on average near-optimal
performance. Finally, using supermarket data we show that our model can lead to
a significant increase in profits.
4 - Dynamic Pricing through Combinatorial Methods
Jeremy Kalas, MIT, Cambridge, United States of America,
Swati Gupta, Kiran Panchamgam,
Georgia Perakis, Maxime Cohen
We explore fast combinatorial methods for the multi-period, multi-item
Promotion Optimization Problem under general demand functions that depend
on past prices. As the problem is NP-hard for large memory, we consider an
approximation via the reference price model, and give a PTAS. We extend this
model to handle cross-item effects using a ``virtual” reference price. We report a
projected 4-6% increase in profits on real-world data sets, in collaboration with
the Oracle Retail Science group.
SD46
46-Room 104A, CC
Empirical Operations Management: Services
Sponsor: Manufacturing & Service Oper Mgmt/Service Operations
Sponsored Session
Chair: Robert Bray,
1 - Free Riding and Auto Recalls: An Asymmetric Dynamic Discrete
Choice Game
Ahmet Colak, Northwestern University, 1116 W Loyola Ave
Apt 3S, Chicago, IL, 60626, United States of America, a-
, Robert Bray
We structurally study auto recalls. Two agents can initiate a recall: the
manufacturer and the federal regulator. Initiating recalls is an entry game with
optimal stopping. We unexpectedly find that the regulator has no deterrence
power over the manufacturer, and that the two agents free ride off each other’s
recall efforts. Free riding decreases the regulator’s and manufacturer’s median
recall probabilities by 2.9% and 0.7% respectively, and increases society’s
exposure to defective parts.
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