2016 INFORMS Annual Meeting Program

262 / 561

INFORMS Nashville – 2016
262
3 - An Analysis Of Menus Of Multi-part Tariffs
Ryan Choi, Assistant Professor of Marketing and SCM, Eastern
Michigan University, 300 W. Michigan Ave., College of Business,
Eastern Michigan University, Ypsilanti, MI, 48197, United States,
Taewan Kim
We study which characteristics of three-part tariffs (3PTs) generate greater profit
than two-part tariffs and examine the optimal values of 3PTs. Under an
assumption of consumer heterogeneity and a full extraction of low type segment’s
surplus, the seller can extract more of high type surpluses. Literatures argue that
offering high type contracts only may be more profitable than keeping the low
type paying high information rent. Since the firm can charge greater rent from
the high type, offering 3PTs contracts to both high and low segments will be more
profitable even though the taste parameter is extremely low, regardless of the
proportion of the low type.
TA87
Broadway A-Omni
Panel: Guide to the Analytics Body of
Knowledge (ABOK)
Sponsored: Analytics
Sponsored Session
Moderator: Louise Wehrle, INFORMS, 5521 Research Park Drive,
Catonsville, MD, 21228, United States,
1 - Guideto The Analytics Body Of Knowledge (ABOK)
Louise Wehrle, INFORMS, 5521 Research Park Drive, Catonsville,
MD, 21228, United States,
The Guide to the Analytics Body of Knowledge (ABOK) is being created in
support of the Certified Analytics Professional (CAP®) program. The ABOK will
serve as a central repository for key analytics knowledge, supported by in-depth
subject matter expert interviews and writing as well as formerly unpublished case
studies. Join us to learn more about the Analytics BOK and provide your input to
the creators of this first edition of the ABOK.
2 - Panelist
Terry Harrison, Pennsylvanis State University, University Park, PA,
16802, United States,
3 - Panelist
James Cochran, University of Alabama, Culverhouse College of
Commerce & Bus Admin, Tuscaloosa, AL, 35487, United States,
TA88
Broadway B-Omni
Service Science Best Student Paper Competition I
Award Session
Chair: Robin Qiu, Penn State University, 30 E. Swedesford Road,
Malvern, PA, 19355, United States,
1 - Bike-share Systems: Accessibility And Availability
Ashish Kabra, INSEAD, Boulevard de Constance, Fontainebleau,
77305, France, Elena Belavina,
Karan Girotra
This paper estimates the relationship between ridership of a bike-share system
and its design aspects— station accessibility and bike-availability. Our analysis is
based on a structural demand model that considers the random-utility
maximizing choices of spatially distributed users, and it is estimated using high-
frequency system-use data from the bike-share system in Paris and highly
granular data on sources of bike-share demand. A novel model separates the
long-term and short-term effects of higher bike-availability. Because the scale of
our data render traditional numerical estimation techniques infeasible, we
develop a novel transformation of our estimation problem.
2 - Queues With Redundancy: Is Waiting In Multiple Lines Fair?
Leela Aarthy Nageswaran, Carnegie Mellon University, 308 GSIA,
Tepper School of Business, 5000 Forbes Avenue, Pittsburgh, PA,
15213, United States, , Alan Scheller-Wolf
We study the performance of two queues serving two classes of customers, one of
which is redundant: a redundant customer joins both queues simultaneously and
is “served” when any one of her copies completes service. Applications of
redundancy range from supermarkets with multiple checkout lines to multiple
listing for kidney transplants. By analyzing different policies that a non-redundant
customer may use to join a queue when faced with different levels of system
information, our model provides fundamental insights on optimal queue-joining
policies and on fairness in such systems.
3 - Online Decision-making With High-dimensional Covariates
Hamsa Sridhar Bastani, Stanford University, 10 Comstock Circle,
Apt 304, Stanford, CA, 94305, United States,
Mohsen Bayati
Big data has enabled decision-makers to personalize choices based on an
individual’s observed characteristics. We formulate this problem as a multi-armed
bandit with high-dimensional covariates, and present a new efficient algorithm
that provably achieves near-optimal performance. The key step in our analysis is
proving convergence of the LASSO estimator despite non-iid data induced by the
bandit policy. We evaluate our algorithm using a real patient dataset on warfarin
dosing; here, a patient’s optimal dosage depends on her genetic profile and
medical records. Our algorithm outperforms existing bandit methods as well as
physicians to correctly dose a majority of patients.
4 - An Efficient Algorithm For Dynamic Pricing Using A
Graphical Representation
Swati Gupta, Massachusetts Institute of Technology,
77 Massachusetts Avenue, Cambridge, MA, 02139, United States,
, Maxime Cohen, Jeremy Kalas, Georgia Perakis
We study a multi-period, multi-item pricing problem: maximize the total profit by
choosing feasible prices that satisfy various business rules. We develop a graphical
model that can solve the problem for small memory. We make no assumption on
the structure of the demand. For large memory, we show NP-hardness and
approximate general demand functions using the reference price model. We give
an approximation to solve the latter, extend it to handle cross-item effects among
multiple items using the notion of a virtual reference price. We cluster items into
blocks and incorporate global business constraints, and finally validate our results
on demand models calibrated by real supermarket data.
5 - Evaluating The First-mover’s Advantage In Announcing Real-time
Delay Information
Siddharth Prakash Singh, PhD Candidate, Tepper School of
Business, Carnegie Mellon University, Tepper School of Business,
5000 Fobes Avenue, Pittsburgh, PA, 15213, United States,
, Mohammad Delasay, Alan Scheller-Wolf
We study queueing models of two comparable service providers competing for
market share. The announcer (A) voluntarily provides real-time delay
information, for example on a website. For the non-announcer (N), customers
are only aware of a periodically updated long-term average delay. Customers
make patronage decisions based on available delay information. We investigate
how A, as the first-mover in announcing real-time delay information, influences
market shares and customer delays. We find that when A is not the higher-
capacity provider, she benefits on both market share and delay. However, when A
is the higher-capacity provider, announcing may result in lower market share or
longer delays.
TA89
Broadway C-Omni
Advances in Traffic Flow Modeling
Sponsored: TSL, Intelligent Transportation Systems (ITS)
Sponsored Session
Chair: Pitu Mirchandani, Arizona Statew University, P.O. Box 878809,
Tempe, AZ, 85287, United States,
1 - A Kalman Filter Approach For Dynamic Calibrationof a Simplified
Lower-Order Car Following Model
Kerem Demirtas, Arizona State University, 699 S. Mill Ave. Tempe,
Brickyard Engineering 553, Tempe, AZ, 85281, United States,
Pitu Mirchandani, Xuesong Zhou
In this study, we are interested in dynamic calibration of car following parameters
in order to explore both inter-driver and intra-driver heterogeneity. Specifically,
we offer an augmented state space system for a lower order linear spacing car
following model developed by Newell and implement a modified Kalman filter
algorithm in order to track the leader-follower pairs and simultaneously predict
and estimate the parameters related with the behavior of the following drivers.
The algorithm is tested on the trajectories from NGSIM data and show satisfactory
results. Interpretation of the results and promising future research directions are
given.
2 - Efficient Supply Calibration Of Large-ScaleTraffic Simulators
Kevin Zhang, Massachusetts Institute of Technology, Cambridge,
MA, United States,
In this presentation, we propose a simulation-based optimization algorithm for
the supply calibration of stochastic traffic simulators. We present a metamodel
that combines information from the simulator with a problem-specific analytical
network model. This metamodel is embedded within a derivative-free trust region
algorithm. With this method, we aim to identify transportation-relevant solutions
with improved performance within a strict computational budget. The approach is
validated on a real traffic network; results are presented
TA87