Informs 2015

INFORMS Philadelphia – 2015

SC17

17-Franklin 7, Marriott

Network Optimization

Sponsor: Optimization/Network Optimization

Sponsored Session

Chair: Kelly Sullivan, Assistant Professor, University of Arkansas,

Fayetteville, AR, 72701,

ksulliv@uark.edu

1 - A Decomposition Approach for Dynamic Network

Interdiction Models

Chase Rainwater, University of Arkansas, 4207 Bell Engineering

Center, Fayetteville, AR, United States of America,

cer@uark.edu

Forough Enayaty Ahangar

This work details the development of a large-scale optimization approach for

solving dynamic bilevel network interdiction problems. A benders decomposition

approach is proposed that utilizes constraint programming to exploit the

scheduling nature of the network interdiction subproblem solved over a finite

time horizon. Computational results comparing the proposed approach to

traditional constraint programming and mixed-integer programming approaches

are discussed.

2 - Supply Chain Design through Acquisition:

A Robust Multi-objective Approach

Amin Khademi, Assistant Professor, Clemson University, 130-D

Freeman Hall, Clemson University, Clemson, SC, 29634, United

States of America,

khademi@clemson.edu,

Mariah Magagnotti,

Scott Mason

Combining supply chain networks for an acquisition is a complicated process;

making decisions for strategic merging of the supply chains when working with

incomplete or incorrect data is even more so. Our work presents a robust

optimization model that allows the decision maker to adjust for an expected

degree of uncertainty, thus producing solutions that are less responsive to

incorrect or incomplete data without being excessively cautious.

3 - Multiple-scenario Approach for a Dynamic Disaster Relief Routing

Problem with Uncertain Social Data

Emre Kirac, PhD Candidate, University of Arkansas, Department

of Industrial Engineering, Fayetteville, AR, 72701, United States

of America,

ekirac@email.uark.edu

, Ashlea Milburn

Social media may play an important role and improve situational awareness in

disaster response by providing real-time information. We present decision support

models capable of considering input streams from social data when planning for

disaster response. Specifically, a dynamic routing problem is presented in which

social data and information from trusted sources are available and change over

time. Alternative decision policies are presented and compared across a variety of

request scenarios.

4 - The Wireless Network Jamming Problem Subject to Protocol

Interference

Hugh Medal, Mississippi State University, Industrial & Systems

Engineering, Starkville, United States of America,

hugh.medal@msstate.edu

We study a wireless network jamming problem, solving it using a cutting plane

approach that is able to solve networks with up to 81 transmitters. Our study

yields the following insights into wireless network jamming: 1) increasing the

number of channels is the best strategy for designing a robust network, and 2)

increasing the jammer range is the best strategy for the attacker.

SC18

18-Franklin 8, Marriott

Data Mining for Healthcare

Cluster: Modeling and Methodologies in Big Data

Invited Session

Chair: Daehan Won, University of Washington, Seattle, 1415 NE

Ravenna Blvd, #401, Seattle, WA, 98105, United States of America,

wondae@uw.edu

1 - General Framework for Rulebased Medical Diagnosis and

Decision Making

Chunyan (sally) Duan, Tongji University & University of

Washington, A503, Sino-French Center, Tongji University,

No.1239, Siping Road, Shanghai, 200092, China,

duanchunyan87@gmail.com,

Daehan Won, Ying Lin,

Shuai Huang, Jianxin You, W. Art Chaovalitwongse

A new mixed integer programming model is developed to select rules with

minimization of prediction errors and control the costs of the features in the

framework. The Diabetes Prevention Trial-Type 1 dataset is used to evaluate and

compare our model with other State-of-the-Art methods. The results show that

our model not only dramatically reduce the number of selected features and

control the costs of the features, but also has a promising accuracy in medical

diagnosis.

2 - Networked Data Classification with Node Selection

Daehan Won, University of Washington, Seattle, 1415 NE

Ravenna Blvd, #401, Seattle, WA, 98105,

United States of America,

wondae@uw.edu

We present a framework for classification of networked structure data . Due to

the huge size of the network, we apply a feature selection scheme. Instead of

general feature selection methods, we present a node selection scheme to

determine the most relevant sub-networks which might yield insightful

information underlying complex networks.

3 - A Structural Model and Bayesian Estimation of New

Technology Adoption

Sebastian Souyris, PhD Candidate, The University of Texas at

Austin, 2110 Speedway Stop B6500, Austin, TX, 78712, United

States of America,

sebastian.souyris@utexas.edu,

Jason A. Duan,

Anant Balakrishnan, Varun Rai

We present a structural model and estimation algorithm to analyze the adoption

of a new technology at individual consumer level. The model incorporates

networks, e.g. geographical distance, to estimate the potential effect of word of

mouth by assuming that the previous adoptions affect the predisposition of a

consumer towards the technology. For inference, we use a Bayesian algorithm

that overcomes the computational burden of classical estimation methods of

structural models.

SC19

19-Franklin 9, Marriott

Application in Transportation Systems

Sponsor: Computing Society