2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
395
5 - Home Health Care Routing And Appointment Scheduling With
Stochastic Service Durations
Yang Zhan, Shanghai Jiao Tong University, Shanghai, 200030,
China, , Zizhuo Wang, Guohua Wan
Motivated by the practice of home health care services, we consider an integrated
routing and appointment scheduling problem with random service durations. The
objective of the problem is to determine the visit route and appointment times to
minimize the summation of the costs of traveling and idling of the health care
team and the cost of waiting of the patients. To solve the intractable problem, we
propose both exact and approximate algorithms. We conduct computational
experiments to assess the performance of the proposed methods on problems of
practical size.The computational results show that the methods work very well.
Wednesday, 10:00AM - 10:50AM
Keynote Wednesday
Davidson Ballroom A-MCC
The Goals of Analysis are Understanding, Decisions,
and Influencing Policy
Invited: Keynote
Invited Session
Chair: Turgay Ayer, Georgia Institute of Technology, Atlanta, GA
(Healthcare Analytics Chair; ayer
1 - The Goals Of Analysis are Understanding, Decisions, And
Influencing Policy
Gerald G. Brown, Naval Postgraduate School, Monterey, CA,
United States,
While we are variously skilled at applying a diverse set of mathematical tools to
analysis, we all share (or should share) the same goals: understand the problem at
hand; advise decisions influencing that problem; and influence policy for dealing
with entire classes of problems resembling the one we analyze. Sometimes, our
answers are not welcomed by a client who brings us a problem, and we face
significant obstacles to conveying good, convincing advice and thus contributing
to good decision policy. There are a number of techniques that apply to such
situations and cross all our various analysis domains. Few of these appear in
textbooks or our open literature. These turn out to be vitally important for
success.
Keynote Wednesday
Davidson Ballroom B-MCC
Can Prediction be Better than Cure? On Analytics
in Health-Care
Invited: Plenary, Keynote
Invited Session
Chair: Walt DeGrange, CANA Advisors, Chapel Hill, NC,
1 - Can Prediction be Better than Cure? On Analytics In Health-Care
Edmund Jackson, Clinical Services Group, HCA, Nashville, TN,
United States,
Healthcare is different: the intrinsic complexity, absolute moral imperatives and
regulatory oversight of this business are unique. As such many of the
technologies in healthcare differ from other industries. That said, the industry is
entering a new regime where data is widely available, technology exists for
analytics to run in real-time and the intention of bringing this intelligence into
the workflow is widespread. Moreover, the advent of techniques such as
diagnostic, predictive, and prescriptive analytics in other industries have ready
applications in healthcare. The potential benefits of these activities to all
stakeholders in the healthcare system, such as patients, providers and payers are
enormous. In this talk Dr Edmund Jackson, Vice President and Chief Data
Scientist of HCA will discuss this topic and provide a perspective of what has
already been achieved and what is soon to come.
Keynote Wednesday
Davidson Ballroom C-MCC
SportSource Analytics
Invited: Plenary, Keynote
Invited Session
Chair: James Primbs, California State University Fullerton, 925
Berenice Dr, Brea, CA, 92821, United States,
1 - SportSource Analytics
Stephen Prather, SportSource Analytics, Nashville, TN,
United States,
Think back about 15 years ago about how difficult it was for anyone to access
large amounts of data on virtually any subject. Now, think about how easy it is
today for virtually anyone to access enormous amounts of data with the click of a
few buttons. We live in an extremely data rich world. We are surrounded by
information and data in all walks of life. The problem with all of this “big data” is
that we are really struggling in finding ways to make it small and more
importantly make it USEFUL. My talk is going to be about how four guys all
working full-time jobs and without a single advanced degree in any sort of
statistical analysis between them were able to become the official analytic
consultant to the college football playoff selection committee. This is a story of the
pursuit of being useful and understanding that data is only as good as the analysis
associated with it.
Wednesday, 11:00AM - 12:30PM
WB01
101A-MCC
Pattern Recognition Applications in Data Mining
Sponsored: Data Mining
Sponsored Session
Chair: Cory Stasko, Massachusetts Institute of Technology, 4 Garden
Court, Apt 4, Cambridge, MA, 02138, United States,
1 - Auto Detection Of Tool Wear Using Sequence
Alignment Technique
Cheng-Bang Chen, Penn State University, 445 Waupelani Dr., Apt
K18, State College, PA, 16801, United States,
Dika Handayani, Deepak Agrawal, Juxihong Julaiti
Tool wear is one common criteria used to measure the machinability of a
material. Manual tool wear measurement, which is still widely done, raises an
issue on how reproducibility and repeatability the measurements are. In order to
reduce the variation of the measurement and speed up the process, we propose a
new method using edge detection, sequence mapping, and area projection to
measure the wear automatically.
2 - Mini-batch Proximal Semi-stochastic Gradient Descent In
Signal Processing
Jie Liu, PhD Student, Lehigh University, 14 Duh Dr Apt 324,
Bethlehem, PA, 18015, United States, ,
Jakub Konecny, Peter Richtarik, Martin Takac
We propose the mini-batch proximal semi-stochastic gradient descent (mS2GD).
First, we provide convergence results for mS2GD and show that it maintains a
complexity of O((n+ )log(1/ )), comparable to modern stochastic gradient descent
methods such as SVRG, SAG, SAGA. Second, we show that mS2GD benefits from
both mini-batch speedup and the simple parallel implementation. In the
numerical experiments, we first compare different algorithms on public available
datasets; then, we compare mS2GD with different batch sizes to illustrate
efficiency of mini-batching; last, we conduct experiments on one of the popular
signal processing problems—a simple image deblurring problem.
3 - Deconstructing Va Procurement And Logistics Policy With Natural
Language Processing
Cory Stasko, Massachusetts Institute of Technology,
4 Garden Court, Apt 4, Cambridge, MA, 02138, United States,
Over 120 policy documents of are involved in governing VHA procurement and
logistics. This large volume of active policy makes it difficult for individuals to
understand what exists, where, and how it affects them. Furthermore, the policy
set includes redundancies, missing elements, and other weaknesses. This work
investigates the value of natural language processing in deconstructing and
mapping interrelated policy texts. We describe and organize the logical, linguistic,
and substantive patterns within and between policy documents, thereby
producing a dynamic map of policy evolution that highlights patterns, inter-
dependencies, conflicts, ambiguities, and redundancies in the text.
WB01