Informs 2015

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INFORMS Philadelphia – 2015
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2 - New Strategies for Quantifying the Resilience of Supply Chains to
Temporally Distinct Disruptions
Jacqueline Griffin, Assistant Professor, Northeastern University,
334 Snell Engineering Center, 360 Huntington Ave, Boston, MA,
02125, United States of America, ,
Ozlem Ergun, Shiqing Liu
The saline supply chain network flow formulation applied for a multi-level supply
chain with lead time between each level and concerning about how factors would
influence each other in different time periods. We present closed form expressions
to characterize the resilience of a supply chain network to varying combinations
of temporally distinct disruptions.
3 - Exploring Strategies for Private Sector Transportation in Uganda
Jarrod Goentzel, MIT, 77 Massachusetts Avenue, Cambridge, MA,
02139, United States of America,
Mark Brennan, Emily Gooding
New product technology is commonly introduced in developing countries
through subsidized pilot programs run by non-governmental organizations
(NGOs). Low landed cost is key for further scaling up product distribution
through the private sector. This study uses a pilot program for agricultural storage
products in Uganda to explore strategies to reduce transportation cost.
4 - Tracking Healthcare Associated Infections at Individual Level over
Dynamic Human Networks
Ziye Zhou, The Chinese University of Hong Kong, William M W
Mong Engineering Bldg., Hong Kong, Hong Kong - PRC,
, Chun-hung Cheng, Dobin Ng
Healthcare associated infections (HAIs) have become a major challenge to public
healthcare. This work addresses the problem of tracking the transmission of HAIs
at an individual level. We present a framework with three key components of
time-varying contact network construction, individual-level transmission tracking
and HAI parameter estimation. Experiments on human positioning data collected
in a four-month tracking study in a hospital are conducted to evaluate the
performance.
MA37
37-Room 414, Marriott
Health Care Modeling and Optimization VI
Contributed Session
Chair: Md Noor E Alam, Post Doctoral Fellow, Massachusetts Institute
of Technology, 135 Quincy Ave, Apt 204, Quincy, MA, 02169,
United States of America,
1 - Shift Scheduling for an Anesthesiology Residency Program
Hernan Abeledo, Associate Professor, George Washington
University, 800 22 St. NW, Washington, DC, 20052, United States
of America, Michael Kanter, Ian Morgan,
Jean - Max Buteau, Liam Nealon
Creating shift schedules for resident physicians is a notoriously difficult task that
is typically done manually by the chief residents. Shift assignments need to
observe a large number of rules, as well as adhere to fairness and desirability
factors while populating a very complex schedule structure. We present an
integer programming model developed to schedule anesthesiology residents at the
George Washington University Hospital.
2 - Shift Scheduling for Medical Residency Programs
Anthony Coudert, George Washington University, 800 22 St. NW,
Washington, DC, United States of America,
Hernan Abeledo
Creating shift schedules for resident physicians is a tedious task that is typically
done manually by the chief residents. Shift assignments need to observe a large
number of rules, as well as adhere to fairness and desirability factors while
populating a very complex schedule structure. We present integer programming
models used to schedule residency programs at the George Washington University
Hospital.
3 - Open-access Outpatient Clinic Scheduling
Yu Fu, ISEN Dept. Texas A&M University, 3131 TAMU, College
Station, TX, 77843, United States of America,
Amarnath Banerjee
This study aims at exploring cost-efficient offline and online scheduling methods
under the open access policy which allows the visits of the same-day-request
patients and walk-in patients as compensation for no-shows of regular patients to
improve clinic performance and revenue benefit. The offline scheduling uses
approximation and heuristic methods on scenarios and data generated by
prediction and simulation. The online scheduling relies on heuristic methods and
stochastic programming models.
4 - Integer Linear Programming Based Statistical Techniques for
Causal Inference
Md Noor E Alam, Post Doctoral Fellow, Massachusetts Institute of
Technology, 135 Quincy Ave, Apt. 204, Quincy, MA, 02169,
United States of America, , Cynthia Rudin
Organizations are fiercely struggling to realize valuable information from large-
scale data that are increasingly used for understanding important cause and effect
relationships. This research developed a methodological frameworks to solve such
critical problems with ILP based statistical techniques. One of the key idea is to
develop robust techniques to handle uncertainty in data driven decision making,
particularly as applied to healthcare.
MA38
38-Room 415, Marriott
Applied Probability I
Contributed Session
Chair: Giang Trinh
Senior Research Associate, Ehrenberg-Bass Institute, University of
South Australia, 70 North Terrace, Adelaide, SA, Australia,
1 - Value of Communication in a One-leader, Two-followers Partially
Observed Markov Game
Yanling Chang, PhD Candidate, Georgia Institute of Technology,
765 Ferst Dr, Atlanta, GA, 30332, United States of America,
Alan Erera, Chelsea White
We consider a one-leader, two-followers partially observed Markov game and
analyze how the value of the leader’s criterion changes due to changes in the
communication quality between the two followers. We present conditions under
which the value of the leader’s criterion degrades or improves, as a function of
this communication quality and the type of game (collaborative or non-
collaborative).
2 - Multi-period Corporate Survival Probability Estimation with
Stochastic Covariates
Ahmad Reza Pourghaderi, Assistant Professor, Abdullah Gul
University, Department of Industrial Engineering,
Melikgazi, Kayseri, 38039, Turkey, ,
Ebrahim Sadreddin
We propose an econometric method to obtain maximum likelihood estimation of
multi-period corporate survival probabilities conditional on macroeconomic and
firm-specific covariates. We then provide an empirical implementation of the
proposed method for about 300 Iran-listed Industrial firms. Our method combines
traditional duration analysis of the dependence of default intensity on time
varying covariates with time-series analysis of covariates.
3 - Managing Capacity with Optimal Buffer Size Selection
Melda Ormeci Matoglu, University of New Hampshire, 10
Garrison Ave., Durham, NH, 03824, United States of America,
We model the problem of managing capacity and determining optimal buffer size
in a BTO environment as a Brownian drift control problem. We seek a policy that
minimizes long-term average cost. The controller can, at some cost, shift the
processing rate among 2 rates and has the option of rejecting orders and idling.
We show that the optimal policy follows a simple policy and determine the
optimal policy parameters. We also calculate important policy performance
metrics.
4 - Modeling and Predicting Purchasing Behavior with an Erlang-2
Poisson Lognormal Model
Giang Trinh, Senior Research Associate, Ehrenberg-Bass Institute,
University of South Australia, 70 North Terrace, Adelaide, SA,
Australia,
We note some practical and theoretical shortcomings of the Erlang-2 Poisson
gamma mixture model or the condensed NBD, which has been successfully
employed for modeling and predicting consumer purchases. We develop a new
model, the Erlang-2 Poisson lognormal mixture model, which has a sounder
theoretical base. We derive the conditional expectation of the new model and use
it to predict future purchases. We show that the new model predicts future
purchases better than the condensed NBD model.
MA38