Informs 2015

154 / 552

INFORMS Philadelphia – 2015
152
3 - Optimal Screening Policies for Women at High Risk of
Breast Cancer
Caglar Caglayan, Georgia Institute of Technology, Atlanta, GA,
United States of America, , Turgay Ayer,
George Rust
Women with breast density, family history of breast or ovarian cancer, or BRCA1
and BRCA2-mutation-carriers are at higher risk of breast cancer. For such
women, non-mammographic modalities such as ultrasound or MRI, adjunct to or
instead of mammogram, can be beneficial but they lead to an increased screening
cost. Considering both potential health benefits and financial aspects, we study
this multi-modality breast cancer screening problem and identify cost-effective
optimal screening policies.
4 - Modeling Supply Chain System Structure to Trace Sources of
Food Contamination
Abigail Horn, PhD Candidate, Engineering Systems Division,
Massachusetts Institute of Technology, 77 Massachusetts Ave.,
E40-240, Cambridge, MA, 02139, United States of America,
, Richard Larson, Stan Finkelstein
We are developing a methodology to identify high probability sources of
contamination in the event of large-scale outbreaks of foodborne disease based on
a graph-theoretic Bayesian inference algorithm. We present results from 2
modeling frameworks used to develop the inference algorithm: analytical models
of stylized versions of the problem leading to new, general insights, and a
Bayesian Network model used to support decision making and targeted
information gathering during investigations.
MA22
22-Franklin 12, Marriott
New Advances in Stochastic Networks
Sponsor: Applied Probability
Sponsored Session
Chair: Guodong Pang
Assistant Professor, Penn State University, 363 Leonhard Bldg,
University Park, PA, 16802, United States of America,
1 - Networks with Several Types of Interacting Tasks
Yuri Suhov, Professor, University of Cambridge/Penn State
University,
I am going to present and discuss new analytic results on a class of reversible
networks with several types of interactive customers. The main result is product-
type formulas for the equilibrium distributions.
2 - Staffing Large-scale Service Systems with Stochastic
Arrival Rates
Ying Chen, PhD Candidate, University of Texas at Austin,
1 University Station, ETC 5.112, Austin, TX, 78712,
United States of America, John Hasenbein
We minimize the staffing level for large-scale queueing systems with random
arrival rates, where a QoS constraint is enforced on the probability a customer is
queued. For single-station systems, we consider an Erlang-C model with only
partial information provided for the discrete arrival-rate distribution, and we
present a numerically stable procedure to obtain asymptotically optimal results. In
the multi-station case, we introduce a joint QoS constraint and explore the
corresponding solutions.
3 - Ergodic Control of Multiclass Multi-pool Networks in the
Halfin- Whitt Regime
Ari Arapostathis, Professor, University of Texas at Austin,
Electrical and Computer Engineering, Austin,
United States of America, , Guodong Pang
We study the scheduling and routing control of Markovian multiclass multi-pool
networks under the long-run average (ergodic) cost criteria in the Halfin-Whitt
regime. We develop a new framework to study the associated ergodic diffusion
controls and characterize the optimal solutions via the HJB equations. The
asymptotic optimality results are established via a spatial truncation technique to
approximate the solutions to the HJB.
4 - Pricing Server Information in Distributed Systems
Mauro Escobar, Columbia University, 500 W 120th Street,
3rd Floor, New York, NY, 10027, United States of America,
, Mariana Olvera-Cravioto
We consider a queueing network where each job consists of a random number of
pieces to be served in parallel, and such that all the pieces of a same job must
begin their processing simultaneously. We analyze the performance of two
models, one where the different pieces are routed to a random subset of servers
and another one where they are optimally assigned to the servers with the
shortest workloads. We illustrate how to use these two models to evaluate
intermediate routing policies.
MA23
23-Franklin 13, Marriott
Queues: Approximations and Control
Sponsor: Applied Probability
Sponsored Session
Chair: Amy Ward, Professor, University of Southern California,
Marshall School of Business, Los Angeles, CA, 90089,
United States of America,
Co-Chair: Mor Armony, NYU Stern, 44 West 4th Street, New York, NY,
10012, United States of America,
1 - Stein’s Method for Diffusion Approximations of
Many-Server Queues
Anton Braverman, Cornell University, Ithaca, NY, 14850,
United States of America, , J. G. Dai
Diffusion approximations for many-server queues have been studied extensively
since the pioneering work of Halfin and Whitt (1981). We focus on many-server
queues that have phase-type service time distributions and exponential customer
patience distributions. We establish rate of convergence of the steady-steady
distribution of a many-server queue in the Halfin-Whitt regime. Our proof
technique connects naturally with Stein’s method for establishing normal
approximations.
2 - Optimal Traffic Schedules
Harsha Honnappa, Perdue University, West Lafayette, IN,
United States of America, Rami Atar,
Mor Armony
We consider the problem of optimally scheduling a finite, but large, number of
customers over a finite time horizon at a single server FIFO queue, in the
presence of ‘no-shows’. We study the problem in a large population limiting
regime as the number of customers scales to infinity and the appointment
duration scales to zero. We show that in the fluid scaling heavy-traffic is obtained
as a result of asymptotic optimization. We also characterize the diffusion-scale
optimal schedule.
3 - Collaboration and Multitasking in Networks:
Capacity Versus Queue Control
Jan Van Mieghem, Professor, Kellogg School of Management,
2001 Sheridan Road, 5th Floor, Evanston, IL, 60201, United
States of America,
Itai Gurvich
We study networks where some activities require the simultaneous processing by
multiple types of multitasking human or indivisible resources. The capacity of
such networks is generally smaller than the bottleneck capacity. This paper shows
how this capacity is achieved through, and affected by, dynamic queue control.
Prioritizing specific queues comes at a signicant loss of capacity. We present
policies that balance capacity and queue control while guaranteeing stability and
optimal scaling.
4 - Dynamic Scheduling in a Many-Server Multiclass System with
General Abandonment Distributions
Amy Ward, Professor, University of Southern California,
Marshall School of Business, Los Angeles, CA, 90089,
United States of America,
Our purpose is to understand how the assumed customer abandonment
distribution affects scheduling decisions in a multiclass M/M/N+GI queue. To do
this, we set up and solve an approximating diffusion control problem. We find
that threshold control is in general sub-optimal. For two classes, we establish
conditions for the optimal policy to have a “U-shape”, instead of the step function
that characterizes a threshold policy.
MA24
24-Room 401, Marriott
Social Media Analytics and Big Network Data
Sponsor: Artificial Intelligence
Sponsored Session
Chair: Bin Zhang, Assistant Professor, University of Arizona,
Department of MIS, Tucson, AZ, 85721, United States of America,
1 - Predicting Hacker IRC Participation using Discrete-time Duration
Modeling with Repeated Events
Victor Benjamin, University of Arizona, 1130 E Helen St,
Room 430, Tucson, AZ, 85719, United States of America,
Bin Zhang, Hsinchun Chen
Literature documents the existence of many active online hacker communities
containing thousands of users. Some participants are expert cybercriminals, but
MA22