Informs 2015

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INFORMS Philadelphia – 2015
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4 - An Inventory Control Model for Modal Split in Transportation:
A Tailored Base-Surge Approach
Chuanwen Dong, Kuehne Logistics University,
Grosser Grassbrook 17, Hamburg, Germany,
, Sandra Transchel, Kai Hoberg
We study an inventory control problem where a firm ships products from a plant
to a distribution center via two transportation modes, a slow and cheap mode
(e.g., rail) and fast but expensive mode (e.g., truck). In the slow mode, products
are shipped with a constant volume, whereas fast-mode ordering follows a base-
stock policy every period. We extend the tailored base-surge (TBS) policy to less
frequent slow-mode shipments and present an approximated analytical solution
approach.
5 - Optimal Time to Reposition Inventories in Multi-location
Centralized Networks
Olga Rusyaeva, Kuehne Logistics University,
Grosser Grasbrook 17, Hamburg, Germany,
, Joern Meissner
Repositioning of inventories between locations aims to decrease the impact of
inventory imbalance caused by e.g. imperfect demand information or delayed
delivery. In practice, it is often done via lateral transshipments. Our dynamic
transshipment policy answers questions – when within the order cycle, how
much, and from which location to transship to maximize the revenue of the
network. A myopic policy and a near-optimal policy based on approximation are
suggested for real-size problems.
WA04
04-Room 304, Marriott
Economics II
Contributed
Chair: Maryam Razeghian, Doctoral Student, EPFL, CDM-ODY 4.16,
Station 5, Lausanne, VD, 1015, Switzerland,
1 - Discrete Choice Modeling Approach to Decide The Digital Divide
Policy Issue
Subhabrata Bapi Sen, Adjunct Faculty, Sillberman College of
Business, 32 Rolling Hill Dr, Chatham, NJ, 07928,
United States of America,
Discrete choice framework to address the digital divide issue as reported in NY
Times story “F.C.C. Chief Seeks Broadband Plan To Aid The Poor” Bridging Digital
Divide. To focus on the main issue - we need to access to Broadband demand
using a conditional logit formulation. The service attributes/demographics will be
explanatory variables. June 6, 2015 in The Wall Street Journal article “Is the U.S.
Ready to pay for ‘Quad Play’? is analyzed here.
2 - On the Relevance of Probability Distortions in the Extended
Warranties Market
Mike Abito, Assistant Professor, University of Pennsylvania
(Wharton), 3620 Locust Walk, SHDH 1407, Philadelphia, PA,
19104, United States of America,
We study the reasons for high profits in the extended warranties market. Using
data from a big US consumer electronics retailer, we find that overweighting of
failure probabilities is a relevant factor in determining economic outcomes:
without probability overweighting, profits drop by 90% and consumer surplus
more than doubles. We also find that overweighting is affected by the
environment and is reduced with learning.
3 - To Share or Not to Share: Adjustment Dynamics in
Sharing Markets
Maryam Razeghian, Doctoral Student, EPFL, CDM-ODY 4.16,
Station 5, Lausanne, VD, 1015, Switzerland,
, Thomas Weber
To describe and further predict the growth of sharing markets, we construct a
model for the dynamic sharing decisions of heterogeneous suppliers in a market
with frictions, allowing for a mismatch between supply and demand. In each time
period, an agent can enter or leave the sharing market, subject to an adjustment
cost. We provide a closed-form expression for the nonlinear evolution of the
rational-expectations equilibrium in this economy, typically resulting in an S-
shaped diffusion pattern.
WA05
05-Room 305, Marriott
Identifying Sentiment Change and Geographic
Location in Social Media
Cluster: Social Media Analytics
Invited Session
Chair: Chris Smith, TRAC-MTRY, 28 Lupin Lane, Carmel Valley, 93924,
United States of America,
1 - Identifying Changes in Twitter Sentiment
Sam Buttrey, Assoc. Prof., Naval Postgraduate School,
Code OR/Sb, Monterey, CA, 93943, United States of America,
, Jon Alt
This ongoing research demonstrates the application of statistics and machine
learning to identify spatio-temporal changes in population sentiment using 10 TB
of recent Twitter data. It also seeks to compare Twitter sentiment to results of
surveys taken in the same places and times. This comparison may inform uses of
sentiment analysis as an alternative to the use of structured surveys in areas
where surveying is infeasible. Practical difficulties in analyzing big data of this sort
are discussed.
2 - Changes in Network Topography to Predict Social Unrest using
Social Media
Rob Schroeder, Naval Postgraduate School, 526 Union St.,
Monterey, CA, 93940, United States of America,
In recent years, social media has become a common communication medium for
social movements. These social movements are able to interact with members,
sympathizers, and the general public using social media. This research analyzes
how the overall structure of their interactions via Twitter change over time and
compares the changes to planned events by the social movement.
3 - Better Defining Location and Attribute Data in Twitter by Utilizing
Wikipedia Localization Text
Patrick Dudas, Contractor, NPS, 1215 Wisconsin Ave, Pittsburgh,
PA, 15216, United States of America,
Within Twitter understanding users’ geolocation is subject to either the user
geotagging their tweets or a high-level profile location. Working with Wikipedia
and Twitter, we better define locations and their localized names and attributes by
means of Wikipedia’s rich datasets. Parsing Wikipedia, we can produce location
objects and their localized translation of the location around the world, producing
a better means of understandings both the user’s location and voice on Twitter.
4 - Non-linear Dynamics of Human Emotions: Analysis of
Twitter Data and its Implications
Les Servi, The MITRE Corporation, 202 Burlington Road,
Bedford, MA, United States of America, ,
Waldemar Karwowski, Dylan Schmorrow, Nabin Sapkota
Exploration of the extent that human emotions, expressed in Twitter data, have
chaotic and non-linear dynamics has profound implications in its use for
forecasting a population’s mood. This study examines such dynamics through the
analysis of hundreds of thousands of Twitter messages.
WA06
06-Room 306, Marriott
Modeling and Computations in Financial Engineering
Sponsor: Financial Services
Sponsored Session
Chair: Lingfei Li, Assistant Professor, The Chinese University of Hong
Kong, 608 William M.W.Mong Engineering BLD, Shatin, Hong Kong -
PRC,
1 - Long Term Risk: A Martingale Approach
Likuan Qin, Northwestern University, 2145 Sheridan Rd, Tech
C210, Evanston, IL, 60208, United States of America,
, Vadim Linetsky
We extend long-term factorization of the pricing kernel to general semimartingale
environments, without assuming the Markov property. We explicitly construct
long-term factorization in HJM models and affine models and decompose the
market price of Brownian risk into the volatility of the long bond plus an
additional risk premium defining the permanent martingale component in the
long-term factorization.
WA06