2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
138
MA49
211-MCC
Tutorials and Examples of Software and Methods for
Social Media Analytics
Invited: Social Media Analytics
Invited Session
Chair: Theodore T Allen, Ohio State University, Columbus, OH, 43210,
United States,
1 - NLP, LDA, SMERT, k-Means And Efficient Estimation Methods
Zhenhuan Sui, Ohio State University,
We describe some of our recent advances in more efficient estimation for text-
based clustering and topic discovery. Also, we illustrate the capability of VBA code
for text processing and benchmark popular methods including k-means clustering
and Latent Dirichlet Allocation in terms of computational efficiency and accuracy.
2 - Innovative Scheduling And Kriging-Based Optimization
Methods In VBA
Sayak Roychowdhury, Ohio State University,
A suite of techniques for scheduling and simulation optimization is described
based on VBA and our own innovations. Results showing improved performance
compared with alternatives for scheduling and inventory policy-making are
described. Possible roles for the methods support social media analytics are also
described.
3 - Cybersecurity Using Interdiction
Murat Karatas, University of Texas, Manor Road, Austin, TX,
78722, United States, , Nedialko Dimitrov
Recent cyber-attacks on private and public groups highlight the importance of a
proper cybersecurity structure. Having well-structured cybersecurity decreases the
vulnerability of the system. We present a network interdiction model to find the
optimal strategy for a cyber physical network. Our model considers the specifics of
the network structure.
MA50
212-MCC
Life After the PhD: Early Career Development Panel
Sponsored: Minority Issues
Sponsored Session
Moderator: Julie Ivy, North Carolina State University,
111 Lampe Drive, Raleigh, NC, 27695, United States,
1 - Life after The PhD: Early Career Development Panel
Julie Ivy, North Carolina State University,
MA51
213-MCC
New Models in Criminology
Sponsored: Public Sector OR
Sponsored Session
Chair: Lawrence Wein, Stanford University, 655 Knight Way, Stanford,
CA, 94305, United States,
1 - Machine Learning For Crime Series Detection And Criminal
Recidivism Prediction
Cynthia Rudin, Duke University, LSRC Building D101, 308
Research Drive Campus Box 90129, Durham, NC, 27708-0129,
United States, , Berk Ustun, Tong Wang
I will discuss machine learning algorithms for two problems: crime series
detection, and recidivism prediction. In crime series detection, the goal is to
identify crimes that were committed by the same individual(s). We cast this as a
clustering problem with cluster-specific feature selection, in joint work with the
Cambridge Police Department. The recidivism prediction problem is cast as a
supervised classification problem, where the goal is to produce a scoring system,
which is a sparse linear model with integer coefficients. This work was a finalist in
the 2015 Doing Good with OR competition, and part of the winning entry of the
2016 Innovative Applications in Analytics Award.
2 - Optimizing Ballistic Imaging Operations
Can Wang, Stanford University, Stanford, CA, United States,
, Mardy Beggs-Cassin, Lawrence M Wein
Ballistic imaging can solve crimes by comparing images of cartridge casings to a
database of images from past crimes. Many cities lack the capacity to process all of
their images. Using data from Stockton, CA, we allocate limited capacity to
maximize the hit rate. The hit rate can be doubled by giving crime scene evidence
priority over test fires, and ranking cartridge types by their hit rate and processing
evidence from only top-ranking cartridge types.
3 - Using Informed Heuristics For Pretrial Release
Jongbin Jung, Stanford University,
We present a simple and intuitive strategy for creating statistically informed
decision rules that are easy to apply and easy to understand, in the context of
pretrial release. These simple informed heuristics take the form of a weighted
check list and can be applied without the aid of a computer, but perform on par
with state-of-the art machine learning methods. The rules can be readily
constructed with moderate statistics knowledge using common and freely
available software packages, facilitating adoption by practitioners in a wide array
of fields.
4 - Assessing Risk-based Policies For Pretrial Release And Split
Sentencing In Los Angeles County Jails
Lawrence Wein, Stanford University,
Mericcan Usta
Court-mandated downsizing of the CA prison system has led to overcrowding in
CA jails. We model the flow of individuals in the Los Angeles County jail system,
from arraignment through post-sentence supervision. We optimize joint pretrial
release and split-sentencing policies that are based on the type of criminal charge
and the risk category as determined by the CA Static Risk Assessment tool.
Policies that offer split sentences to all low-level felons optimize the trade-off
between public safety and jail congestion.
MA52
214-MCC
Recent Developments in Humanitarian Logistics
Sponsored: Public Sector OR
Sponsored Session
Chair: Kezban Sokat, Northwestern University, 2145 Sheridan Road,
Room C210, Evanston, IL, 60208, United States,
1 - The Vaccination Campaign Routing Problem
Melih Celik, Middle East Technical University, Ankara, Turkey,
, Bahar Cavdar, Haldun Sural
This study considers the routing of vaccination campaigns in developing country
settings, where a team selects from a set of regions to visit and sequences these
visits, subject to special time window constraints. The objective is to maximize the
total number of people reached. A two-stage heuristic is proposed, where the first
stage solves a b-matching problem to determine the regions to visit each day,
whereas the second stage solves a modified orienteering problem to determine
the routes.
2 - Cash, Vouchers Or In-kind Aid: A Game Theory Approach To
Determine Optimal Aid Transfers
Christos Bitos, Kühne Logistics University, Hamburg, Germany,
Maria Besiou
This research aims to determine the conditions for optimal aid transfers in the
aftermath of a disaster or during a long-term development program. By using the
principles of game theory, we consider the strategic interactions between
Humanitarian Organizations (HO) and local markets, and discern how these
interactions affect the distribution of aid to communities. We consider the effects
of local market supply chain fluctuations, and how the market fluctuates in
response to local and national crises. Ultimately, we hope to develop a framework
to contextualize the intricacies of humanitarian relief distribution that can be
applied broadly.
3 - An Agent-based Modeling Approach To Assess Coordination
Among Humanitarian Relief Providers
Jessica Heier Stamm, Kansas State University, Manhattan, KS,
United States, Megan Menth
Coordination between humanitarian organizations during disaster response may
improve efficiency, reduce duplication of efforts, and lead to better outcomes for
beneficiaries. We employ agent-based simulation to examine coordination
strategies among humanitarian organizations that make post-disaster location
decisions regarding temporary service facilities. For example, over 4,000
temporary learning facilities were needed after the 2015 Nepal earthquakes
damaged or destroyed numerous school buildings. Our model is informed by data
from the Nepal response and a survey of humanitarian professionals. We find that
coordination strategies impact efficiency, effectiveness, and equity.
MA49