Informs 2015

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INFORMS Philadelphia – 2015
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3 - An Approach to Estimating Customer Lifetime Values for
Apartment Tenants
Jian Wang, Vice President, Research & Development, The
Rainmaker Group, 4550 North Point Parkway, Alpharetta, GA,
30022, United States of America,
Estimating tenant lifetime values is important for apartment revenue
management. We propose a heuristic approach to predicting renewal likelihoods
and estimating tenant lifetime values. We then present empirical results based on
real apartment data.
TB30
30-Room 407, Marriott
Intelligent Agents and Systems
Contributed Session
Chair: Mohsen Moghaddam, PhD Candidate, Purdue University,
1155 Anthrop Dr., Apt. 9, West Lafayette, IN, 47906,
United States of America,
1 - Optimizing Physician to Patient Consults using Robot-based
Virtual Systems
Henry Ibekwe, Post Doctoral Researcher, Independent,
Richmond, United States of America,
The delivery of quality healthcare for chronically ill patients is burdened by the
limited resources available to physicians and healthcare facilities. We propose the
use of virtual-presence autonomous robot systems to optimize the physician to
patient consultation by minimizing the patient wait-time and maximizing the
number of physician consults given limited resources. We formulate a robot-
patient interaction model as a stochastic process and solve using discrete-time
dynamic programming.
2 - A Study on the Influence of Trust and Distrust Ratings in Social
Networks on Cold Start Users
Sanjog Ray, Assistant Professor, Indian Institute of Management
Indore, Rau Pithampur Road, Indore, 453331, India,
This study examines how cold start users get influenced by the trust and distrust
scores of other users in a social network. We examine the users trusted by cold
start users on the basis of critical parameters: number of trust statements, number
of distrust statements, and number of items rated. We base our findings on our
analysis of the real life Epinions dataset. Our analysis has implications for design
of trust aware recommender systems for cold start users.
3 - Analyzing Inventory Policies in Multi-stage Automatic
Manufacturing Systems
Barin Nag, Professor, Towson University, Department of
E-Business & Technology Ma, Towson, MD, 21252, United States
of America, , Dong-qing Yao, Sungchul Hong
In a multi-stage manufacturing system each stage fills demand from any
combination of buffer inventory or production. Lowest inventory levels may not
be lowest cost, with contradictions arising from the costs of delays of physical
production, backlogs, breakdowns, and bottlenecks. We study best performance
inventory policies using varied production architectures.
4 - A Modeling Framework of Cyber-Physical Systems
Ashutosh Nayak, Student, Purdue University, 318 N Salisbury St,
Apt. 8, West Lafayette, IN, 47906, United States of America,
Shimon Y. Nof, Seokcheon Lee,
Rodrigo Levalle
Effective modelling of CPS is a big challenge. In this work, we propose a resource
sharing based framework for CPS aimed at maximizing its utility. This framework
represents CPS as a network of tasks and resources characterized by utility
functions and overlapping resource communities. A distributed control approach
backed by utility aggregation function is considered for optimality and stability. Its
implementation is illustrated through two examples: Smart factory and multi-
robot system.
5 - Collaborative Networked V-organizations: Design & Integration
Mohsen Moghaddam, PhD Candidate, Purdue University, 1155
Anthrop Dr., Apt. 9, West Lafayette, IN, 47906, United States of
America, , Shimon Y. Nof
Modern distributed, networked, and collaborative organizations of
humans/machines/firms enable systematic integration of distributed resources for
processing dynamic/diverse tasks. We design collaborative networked v-
Organizations by integrating physical (location of resources) and virtual
(allocation of tasks) dimensions, for higher service level, stability, and utilization.
A mixed-integer program and a tabu search are developed for modeling and
optimization purposes, respectively.
TB31
31-Room 408, Marriott
Connected Vehicle Analytics
Sponsor: Data Mining
Sponsored Session
Chair: Juan Li, Member of Research Staff, Xerox Innovation Group,
800 Phillips Road, 128-27E, Webster, NY, 14580,
United States of America,
1 - A System for Estimating Traffic Congestion Measures in a
Network using GPS Smartphone
Charles Chung, Vp Products, Brisk Synergies, 295 Hagey Blvd,
1st Flr, Waterloo, Canada,
A smartphone app is developed for logging route data. A platform is then built for
mapping traffic congestion using speed indicators average speed and speed
differential at the link level. The results demonstrate the feasibility and huge
potential our data collection system that can be implemented in any city and sets
the growth for real-time applications for connected vehicles.
2 - Online Travel Mode Identification with Smartphones
Qing He, Assistant Professor, SUNY Buffalo, 313 Bell Hall,
Buffalo, NY, 14051, United States of America,
Xing Su, Hernan Caceres, Hanghang Tong
We propose an online classification algorithm to detect user’s travel mode using
mobile phone sensors. Our application is built on the latest Android smartphone
with multimodality sensors. By applying a hierarchical classification method, we
achieve high accuracy in a binary classification wheelers/non-wheelers travel
mode, and all six travel modes.
3 - Locating Heterogeneous Traffic Sensors to Improve Network
Surveillance Benefit
Xuechi Zhang, Graduate Research Assistant, University of
Maryland, 0147C Eng Lab Bld, University of Maryland,
College Park, MD, 20742, United States of America,
, Ali Haghani
Optimal placement of traffic sensors is significant to improve urban mobility. In
this study, a mathematical optimization model of deploying heterogeneous
sensors (i.e. Bluetooth sensor and loop detector) to large-scale traffic network is
proposed. Maximizing real-time information report reliability and coverage are
chosen as dual objectives. In addition, the effect of real-time GPS-based probe
vehicle data is also considered. A case study in Washington D.C. area is conducted
for demonstration.
4 - Inferring Trajectories for Partial Observations
Juan Li, Member of Research Staff, Xerox Innovation Group, 800
Phillips Road, 128-27E, Webster, NY, 14580, United States of
America, Moshe Lichman, Padhraic Smyth
The amount of spatial trajectory data is growing fast with the rapid increased
availability of GPS-embedded vehicles. The trajectory data is mixed with high and
low sampling rate with partial observations. In this study, we aim to build
probabilistic models to infer possible traversed route for low sampling rate vehicle
trajectory data.
TB32
32-Room 409, Marriott
Business Analytics in Higher Education Industry
Sponsor: Analytics
Sponsored Session
Chair: Roger Gung, Director, Business Analytics & Operations Research,
University of Phoenix, 3137 E Elwood St, Phoenix, AZ, 85034,
United States of America,
1 - Marketing Mix Optimization
Roger Gung, Director, Business Analytics & Operations Research,
University of Phoenix, 3137 E Elwood St, Phoenix, AZ, 85034,
United States of America,
Marketing spend allocation drives the volume of new marketing inquiries (NMI)
and enrollments. Two-stage non-linear regression models were built to formulate
NMI channels with respect to marketing spends which were defined as either
endogenous, exogenous or instrument variables. The optimization model was
formed by aggregating all NMI channels’ regression models into one objective
function. The optimal spend allocation was then derived from the model every
quarter to guide marketing strategies.
TB30