Informs 2015

327 / 552

INFORMS Philadelphia – 2015
325
TC21
21-Franklin 11, Marriott
Innovations in Healthcare Operations
Sponsor: Health Applications
Sponsored Session
Chair: Mili Mehrotra, University of Minnesota, 321 19th ave south,
minneapolis, United States of America,
1 - Incentizing Less-Than-Fully-Qualified Providers for Early
Diagnosis of Tuberculosis in India
Sarang Deo, Assistant Professor, Indian School of Business
Hyderabad, ISB Hyderabad, Gachibowli, Hyderabad, TS, 500032,
India, , Milind Sohoni, Neha Jha
A major driver of TB epidemic in India is delay in diagnosis by less-than-fully-
qualified providers (LTFQs), who are typically the first point of contact for
patients. This work is motivated by pilots funded by international donors to
provide monetary incentives to LTFQs to induce earlier diagnosis. We develop a
game-theoretic model to design an incentive contract that should be offered to
LTFQs and calibrate it using realistic parameter estimates obtained from primary
and secondary data.
2 - Optimizing Spatiotemporal Antiviral Release Schedules in a
Pandemic Influenza
Bismark Singh, Assistant Professor, University of Texas at Austin,
Austin, TX, United States of America,
Nedialko Dimitrov
To help the state of Texas plan influenza pandemic interventions, we build a
stochastic MIP to compute time-based antiviral releases. We derive scenarios for
the stochastic program from an epidemic simulator that accounts for the large
amount of uncertainty in disease progression. We study the hardness of this
problem, and present models and methods to solve it, even though a direct-solve
is intractable because of the large number of scenarios.
3 - Online Scheduling of Operating Rooms
Chaitanya Bandi, Kellogg School of Management,
Northwestern University, Evanston, IL, United States of America,
Diwakar Gupta
We consider the online operating room scheduling problem where we do not
know the sequence of requests and associated surgery lengths beforehand. Given
the uncertainty and the objective of feasible schedules, we model the uncertainty
using a Robust Optimization (RO) approach, and utilize a RO framework to
develop an interval-classification scheduling algorithm optimized under the RO
framework. We obtain provable lower bounds on the performance and show
promising results based on real data.
4 - Is Technology Eating Nurses? – Staffing Decisions in
Nursing Homes
Feng Lu, Assistant Professor, Purdue University, 403 W State St,
West Lafayette, IN, 47907, United States of America,
, Huaxia Rui, Abraham Seidmann
We study the effect of IT-enabled automation on staffing decisions in healthcare
facilities using a unique nursing home IT data from 2006 to 2012. We also
develop a strategic staffing model that incorporates technology adoption.
TC22
22-Franklin 12, Marriott
Analysis and Control of Queues
Sponsor: Applied Probability
Sponsored Session
Chair: Hayriye Ayhan, Georgia Tech, Atlanta, GA, United States of
America,
1 - Control of Multiserver Energy-aware Queueing Systems
Vincent Maccio, McMaster University, 1280 Main Street West,
Hamilton, Canada, , Douglas Down
We study the problem of controlling a multiple server system, where servers may
be turned on or off. The cost function of interest is a combination of holding costs
and energy costs (and potentially switching costs). We provide several structural
results on the optimal policy - these structural results are enough to allow for the
derivation of the optimal policy for a wide range of systems. Finally, we discuss
how these policies compare with those extant in the literature.
2 - The Snowball Effect of Customer Slowdown in Critical
Many-server Systems
Jori Selen, PhD Candidate, Eindhoven University of Technology,
De Zaale, Eindhoven, Netherlands,
Johan Van Leeuwaarden, Vidyadhar Kulkarni, Ivo Adan
Customer slowdown describes the phenomenon that a customer’s service
requirement increases with experienced delay. In healthcare settings, there is
substantial empirical evidence for slowdown, particularly when a patient’s delay
exceeds a certain threshold. For such threshold slowdown situations, we design
and analyze a many-server system that leads to a two-dimensional Markov
process. Analysis of this system leads to insights into the potentially detrimental
effects of slowdown.
3 - Maximizing throughput in Non-collaborative Networks of Queues
Tugce Isik, Georgia Institute of Technology, 755 Ferst Drive NW,
Atlanta, GA, 30332-0205, United States of America,
, Hayriye Ayhan, Sigrun Andradottir
We study queueing networks with flexible non-collaborative servers. We
introduce a processor sharing (PS) scheme that yields maximal throughput when
buffers are infinite. For systems where the servers cannot work together at a
station, we develop non-collaborative round-robin policies that approximate PS
as the rotation of the servers becomes more frequent. We evaluate the
performance of these policies in queueing networks with tandem, merge, and
split topologies for different buffer sizes.
4 - Optimal Assignment of Authentication Servers to Different
Customer Classes
Daniel Silva, Georgia Tech, 755 Ferst Drive, Atlanta, GA, United
States of America, , Hayriye Ayhan, Bo Zhang
Consider a system where user requests for authentication arrive from several
classes of customers, following independent Poisson processes. Each arrival has a
class-dependent probability of being an impostor. The system has several
authentication methods; each one has a known service time distribution, and a
Type I and II error probability. A controller assigns a method to each user request.
We model the system as a queueing network and find the structure of a cost-
optimal routing policy.
TC23
23-Franklin 13, Marriott
Stochastic Modeling and Control of Production
Systems
Cluster: Stochastic Models: Theory and Applications
Invited Session
Chair: Sanket Bhat, McGill University, 1001 Sherbrooke Street West,
Room 520, Montreal, QC, H3A 1G5, Canada,
1 - Using an Artificial Neural Network Model and Approximate
Dynamic Programming for Stochastic Control
Han Wu, Student, University of Louisville, 2301 S 3rd St,
Louisville, KY, 40218, United States of America,
, Gerald Evans, Kihwan Bae
Development of efficient control policies for dynamic production systems is
difficult. The uncertain demands and large set up times on machines can cause
significant problems. Consider an assembly line for dishwashers which require
multiple types of wire racks that must be fabricated and coated at different
machines. An Artificial Neural Network model is embedded within an
approximate dynamic programming algorithm to search for a better production
and inventory control policy.
2 - Resource Allocation Policies to Provide Differentiated Service
Levels to Customers
Ananth Krishnamurthy, Associate Professor, University of
Wisconsin-Madison, 1513 University Avenue,, ME 3258,
Madison, WI, 53706, United States of America,
, Sanket Bhat
We analyze resource allocation decisions for component manufacturers who
supply components to several original equipment manufacturers (OEMs). OEMs
differ in their demand variability and service level expectations. We derive
policies that provide differentiated service to OEMs depending on their demand
variability. Under the dynamic programming framework, we investigate the value
of these policies to component manufacturers.
3 - A Newsvendor Problem with Price-sensitive and Uncertain Supply
Z. Melis Teksan, University of Florida, ISE Dept. 303 Weil Hall,
P.O. Box 116595, Gainesville, FL, 32611, United States of
America, , Meltem Tutar, Joseph Geunes
We study a newsvendor problem in which the supply quantity depends on the
price offered by the newsvendor to suppliers. We analyze the optimal ordering
policy, which depends on the economics of overage and underage costs, as well as
the relationship between price and supply quantity. We characterize the optimal
supply-pricing policies for cases in which suppliers are also unreliable, i.e., supply
capacity is both price-dependent and random.
TC23