Informs 2015

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INFORMS Philadelphia – 2015
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54-Room 108A, CC
Pricing Inspired by Data and Practice
Cluster: Tutorials
Invited Session
Chair: Georgia Perakis, MIT, 77 Massachusetts Avenue, Cambridge,
MA, 02139, United States of America,
1 - Tutorial: How Analytics Can Impact Promotion Pricing
Georgia Perakis, MIT, 77 Massachusetts Avenue, Cambridge, MA,
02139, United States of America, , Lennart
Baardman, Maxime Cohen, Swati Gupta, Jeremy Kalas, Zachary
Leung, Danny Segev, Kiran Panchamgam, Anthony Smith
Pricing has seen exciting developments in the recent years. A particular area of
pricing that has recently emerged is promotion pricing. In many important
settings such as in grocery retail, promotions are a key instrument for driving
sales. The Promotion Optimization Problem is a challenging problem as the
retailer needs to decide which products to promote, what is the depth of price
discounts, when to schedule the promotions and how to promote the product. We
will discuss our collaboration with Oracle RGBU on how analytics can have a key
impact.
SB55
55-Room 108B, CC
Airline Revenue Management and Customer Choice
Sponsor: Aviation Applications
Sponsored Session
Chair: Emmanuel Carrier, Delta,
1 - Estimation of Airline Itinerary Choice Models using
Disaggregate Ticket Data
Laurie Garrow, Georgia Institute of Technology, Mason Building,
Atlanta, GA, United States of America,
, Virginie Lurkin, Michael Schyns
Airline itinerary choice models support many multi-million dollar decisions, i.e.,
they are used to evaluate potential route schedules. Classic models suffer from
major limitations, most notably they use average fare information but to not
correct for price endogeneity. We use a novel database of airline tickets to
estimate itinerary choice models using detailed fare data and compare these to
classic itinerary choice models that use aggregate fare information but correct for
price endogeneity.
2 - Insights from Mining Airline Booking Data
Catherine Cleophas, RWTH Aachen, Kackertstrasse 7, 52070,
Germany, , Sebastian Vock,
Laurie Garrow
We present a new perspective on airline booking data by comparing data from
geographically and temporally distinct travel itineraries. To this end, we mine
several hundred origin-destination-carrier combinations and several thousand
itineraries. By clustering booking class distributions, we measure market
similarity. The goal is to use cluster-adherence to compute decision trees, so as to
investigate the relevance of traditional assumptions about market differentiation.
3 - Nonparametric Estimation of Demand Structures in Airline
Revenue Management
Johannes Jürg, RWTH Aachen University, Kackertstr. 7, Aachen,
52072, Germany, ,
Catherine Cleophas
A central theme of airline revenue management is analyzing historical booking
data to draw conclusions on the underlying demand structure. This contribution
focuses on the estimation of demand segments present in a market using
nonparametrical methods on panel data. We employ finite mixtures to model
booking events over time frames and to obtain an estimator for the number of
demand segments and their probability distribution over products.
SB56
56-Room 109A, CC
Location Applications
Sponsor: Location Analysis
Sponsored Session
Chair: Mihiro Sasaki, Professor, Nanzan University, 18 Yamazato,
Showa, Nagoya, 466-8673, Japan,
1 - A New Formulation for Energy-efficient Aggregation of Virtual
Machines in Cloud Data Centers
Hajime Miyazawa, Nanzan University, 18 Yamazato-cho,
Showa-ku, Nagoya, 4668673, Japan,
Mihiro Sasaki
One of the main concerns of cloud computing is energy consumption in data
centers. Appropriate aggregation of virtual machines(VMs), which are the
computing entities of cloud computing, on limited number of physical servers in
data centers can reduce energy consumption by shutting down the rest of the
servers. We present a new formulation of assigning VMs among physical servers
to achieve energy-efficient VM aggregation in cloud data centers.
2 - Mathematical Properties of New Indices for Evaluating Spatial
Demand-and-supply Balance
Takamori Ukai, Tokai University, 143 Shimokasuya, Isehara,
2591193, Japan, , Mihiro Sasaki
In this presentation, we show some mathematical properties of new indices for
evaluating spatial demand-and-supply balance. More precisely, we show that the
sequence of solutions generated by the iterative algorithm converges to the
proposed indices. We also discuss the relationship between the solutions and
those obtained by solving a mathematical programming problem with an
objective of minimizing the variance of indices. The objective corresponds to
minimize unfairness among customers.
3 - Optimal Location Model for Anti-piracy Activity in Somalia
Daisuke Watanabe, Associate Professor, Tokyo University of
Marine Science and Technology, 2-1-6 Etchujima, Koto-ku,
Tokyo, 135-8533, Japan, , Richard Church
A significant number of pirate attacks have occurred off the coast of Somalia. This
is a major threat to ships navigating the major sea lane between Asia and Europe.
The purpose of this study is to analyze the optimal location for anti-piracy activity
in Somalia using the maximal covering location model.
4 - Optimal Real-time Pricing of Electricity for Supply and Demand
Control in a Smart Community
Mihiro Sasaki, Professor, Nanzan University, 18 Yamazato,
Showa, Nagoya, 466-8673, Japan,
Yoichi Tanaka, Yasuaki Oishi, Masao Fukushima,
Masaki Yasunishi
We consider a smart community where the supplier of electricity presents the
electricity price varying with different time, and consumers determine their own
optimal levels of buying/selling electricity in responding to the time-varying price.
We formulate the problem of finding supplier’s optimal pricing as a bilevel
programming problem. Computational results show that real-time pricing can
effectively control supply and demand of electricity.
SB57
57-Room 109B, CC
Electricity Market Models
Sponsor: ENRE – Energy I – Electricity
Sponsored Session
Chair: Hung Po Chao, Energy Trading Analytics, 2842 Main St.,
Suite 206, Glastonbury, CT, 06033, United States of America,
1 - Decentralized Markets with LMP for Efficient Congestion
Management of Renewable Electricity Feed-in
Hans Schermeyer, Research Associate, Karlsruhe Institute of
Technology, Hertzstr. 16, Karlsruhe, 76187, Germany,
, Valentin Bertsch, Wolf Fichtner
In this work, we analyse electricity grid congestion caused by renewables and
their necessary curtailment on distribution grid level. To explore the possibilities
of enhanced congestion management, we develop an agent-based simulation
model that represents a distribution grid in Germany which faces frequent
congestion caused by renewables. Inspired by (Distribution) Locational Marginal
Pricing theory we implement a decentralized market design for a more efficient
congestion management.
2 - Operational Flexibility in Dutch Electricity Markets
Robin Broder Hytowitz, Johns Hopkins University, 3400 North
Charles Street, Ames 313, Baltimore, MD, United States of
America, , Ozge Ozdemir,
Benjamin Hobbs
A two-stage study is performed to evaluate the impact of renewable energy on
operational flexibility in the Dutch market given current demand projections. A
unit commitment model for a pan-European network simulates the day-ahead
market, and the successive balancing market is simulated for the Netherlands
alone. The two stages are run for several business cases to analyze resources that
can provide further flexibility, including demand-side management, storage, and
reserve constraints.
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