Informs 2015

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INFORMS Philadelphia – 2015
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4 - Optimal Post-donation Blood Screening under Prevalence
Rate Uncertainty
Hadi El-amine, PhD Student, Virginia Tech, 250 Durham Hall
Perry St., Blacksburg, VA, 24061-0118, United States of America,
, Ebru Bish, Douglas Bish
Blood product safety, in terms of being free of transfusion-transmittable
infections, is crucial. Under prevalence rate uncertainty, various objective
functions, including minimization of a mean-variance objective and minimization
of the maximum regret, were considered in order to determine a “robust” post-
donation blood screening strategy that minimizes the risk of releasing an infected
unit of blood into the blood supply. Efficient and exact algorithms are provided.
SD36
36-Room 413, Marriott
Public and Nonprofit Sector Applications
Sponsor: Public Sector OR
Sponsored Session
Chair: Ece Zeliha Demirci, PhD Candidate, Bilkent University,
Department of Industrial Engineering, Bilkent University, Ankara,
06800, Turkey,
1 - Designing Intervention for Public-interest Goods
Ece Zeliha Demirci, PhD Candidate, Bilkent University,
Department of Industrial Engineering, Bilkent University,
Ankara, 06800, Turkey, Nesim K. Erkip
We study intervention design problem for public-interest goods with two
intervention tools: investment on demand increasing strategies and subsidies. We
consider a setting composed of a retailer whose demand is exponentially
distributed and a central authority with fixed budget. We characterize the optimal
solution structure and enrich our findings with detailed analysis of results.
2 - A Two-stage Model for Dynamic Staff-job Assignments in the
Non-for-profit Sector
Tina Rezvanian, PhD Student, Northeastern University,
Huntington Ave, Boston, 02115, United States of America,
Ozlem Ergun
We design mechanisms for large-scale assignment problems that appear in public
sector applications by identifying complete, stable, and fair staff-job matchings
over time, even when preference lists are truncated. To address these issues we
consider dynamic and multi-stage negotiation policies using stochastic
optimization. Equilibrium concepts and heuristics are proposed to approximate
the proposed problem to optimality.
3 - Modelling and Analysis of New Zealand (NZ) Legislation Network
Neda Sakhaee, PhD Candidate, University of Auckland, Room
576, 38 Princes Street, Auckland, 1010, New Zealand,
, Golbon Zakeri, Mark Wilson
Network representation of legal documents is a novel approach to study this
complex system. The result is a huge citation network of legal documents (as
nodes) and links between them (as edges). We present this network for NZ acts
using a data set of more than 700 year old acts from NZ legislation website. We
study the structure of the network, measures, clusters and time evolution. Then,
we present correlation studies between the clusters and government policies
considering longitudinal changes
4 - Optimizing Government Resource Allocation to Increase
Community Resilience
Saba Pourreza, PhD Candidate, University of North texas, 1307
West Highland Street, Denton, TX, 76201, United States of
America, , Brian Sauser
This study constructs an optimization model that considers two decision variables
job creation, goods and service production. The aim of the model is to enhance
the community impact of small medium size businesses (SMB) when a disruption
hits.
SD37
37-Room 414, Marriott
Health Care Modeling and Optimization IV
Contributed Session
Chair: Xiang Zhong, University of Wisconsin, 5019 Old Middleton Rd,
Madison, WI, 53706, United States of America,
1 - A Predictive Readmissions Model for Coronary Bypass Artery
Grafting Patients
Jingyun Li, California State University Stanislaus, 7740 McCallum
Blvd., Dallas, TX, 75252, United States of America,
, Steves Ring, Indranil Bardhan
Short-term hospital readmissions due to CABG surgery is a burgeoning problem.
Drawing on archival data of CABG patients from 27 hospitals in North Texas,
during a three-year period, our model predicts the 30-day readmission propensity
of CABG patients, as well as their frequency, and time to readmission.
2 - Innovation in Healthcare Management using Data-driven Clinical
Pathways
Yiye Zhang, Carnegie Mellon University, 4800 Forbes Ave,
Pittsburgh, PA, 15213, United States of America,
, Rema Padman
This paper investigates how service innovations in the management of healthcare
delivery can be facilitated through the development of data-driven clinical
pathways. We propose a clinical pathway learning algorithm that models the
association between treatments and patients health conditions as a hidden
Markov model, and also makes predictions for patients’ future states. We
customize clinical pathways by patient and treatment types using hierarchical
clustering and frequent sequence mining.
3 - Analysis of the Impact of Electronic Visits on Patient Care Delivery
Xiang Zhong, University of Wisconsin, 5019 Old Middleton Rd,
Madison, WI, 53706, United States of America,
Jingshan Li, Philip Bain, Albert Musa
To improve care access, many healthcare organizations have introduced electronic
visits to provide patient-physician communication. In this study, we introduce an
analytical model to study the care delivery with e-visits. Analytical formulas to
evaluate the mean and variance of patient length of stay during access to care are
derived. The impact of e-visits on patient access to other care delivery venues is
investigated. Scheduling and control policies to improve care access are discussed.
SD38
38-Room 415, Marriott
Big Data III
Contributed Session
Chair: Munir Majdalawieh, Associate Professor, Zayed University,
Academic City, Dubai, United Arab Emirates,
1 - A Simulation-Optimization Method for Quantitative Aggregation of
Prior Statistical Findings
Mohammad Jalali, Massachusetts Institute of Technology,
Cambridge, MA, 02141, United States of America,
We introduce a simulation-optimization method for quantitative aggregation of
prior statistical findings. The method uses only available statistical results from
prior studies to estimate a meta-model that is consistent with those original
findings. As an empirical demonstration, we aggregate prior studies of the
determinants of basal metabolic rate. Our model proves more accurate than
existing models in the literature and the models by World Health Organization
and Institute of Medicine.
2 - Guaranteed Matrix Completion via Non-convex Factorization
Ruoyu Sun, Stanford University, Menlo Park, CA, 94025,
United States of America, , Zhi-Quan Luo
Matrix factorization is very popular for large-scale matrix completion. However,
due to the non-convexity, there is a limited theoretical understanding of this
approach. We show that under similar conditions to those in previous works,
many standard optimization algorithms converge to the global optima of the non-
convex factorization based formulation, thus recovering the true low-rank
matrix.Our result is the first one that provides exact recovery guarantee for many
standard algorithms.
SD38