2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
291
5 - Optimizing Procurement Of High-value Medical Products In
A Health-care Network
Parimal Kulkarni, Manager, Supply Chain Analytics, BJC
Healthcare, 8300 Eager Rd, Suite 500 D Mailstop 92-92-277,
St Louis, MO, 63144, United States,
Parimal Kulkarni, Manager, Supply Chain Analytics, University of
Missouri, St.Louis, One University Blvd, St Louis, MO, 63121,
United States, L. Douglas Smith, Glen Moser
We use MILP optimization and simulation in concert to develop procurement
strategies for high-value medical supplies in a health-care network. With a multi-
objective MILP model, we determine product groups to be purchased from
alternative vendors to achieve quantity discounts while maintaining diversity of
supply. Considered are physician preference, budgetary limits, and scorecards of
vendor performance on several dimensions. Discrete-event simulation is used
iteratively to test procurement solutions and help set the MILP constraints to keep
risk at acceptable levels.
TB79
Legends G- Omni
Opt, Stochastic II
Contributed Session
Chair: Hernan Andres Caceres Venegas, Ph.D. Student, University at
Buffalo - SUNY, 342 Bell Hall, University at Buffalo, Buffalo, NY, 14260,
United States,
1 - Efficient Solving Of Multi-stage Mixed-integer Stochastic
Problems Under Mean-dispersion Distributional Information
Krzysztof Postek, PhD Candidate, Tilburg University, Warandelaan
2, Tilburg, 5037 AB, Netherlands, ,
Ward Romeijnders, Dick den Hertog, Maarten H van der Vlerk
We propose a solution method for multi-stage robust optimization and stochastic
programming problems under distributional uncertainty, when the means and
mean absolute deviations of the parameters are known. Using new theoretical
results we show for problems with integer recourse how to construct good convex
approximations with known performance bounds and how to solve these
problems efficiently. Our approach gives insights into the performance of the
various recourse rules, the value of distributional information, and the trade-offs
between different variants of the objective function (worst-case, worst-case
expected, best-case).
2 - Multi-project Scheduling With Multi-mode Resource Constrained
Under Uncertainty
Berna Dengiz, Professor, Baskent University, Eskisehir Road
20th Km, Baglica Campus, Ankara, 06530, Turkey,
Serdar Soysal
In this study, we address a resource constrained project scheduling problem
including uncertainties in resource usage rate in a multi-project environment.
The activities of each project have alternative resource usage modes. Resources
are dedicated to all projects considering their dedication policy. The projects
involve finish to start zero time lag, nonpreemptive activities and limited
renewable and nonrenewable resources. In this study, the optimal dedication of
resource capacities to the projects and minimum value of weighted tardiness over
all projects will be determined by proposed solution approach.
3 - Stochastic Integer Programming With Endogenous Uncertainty In
Open Access Outpatient Clinic Appointments Scheduling
Amarnath Banerjee, Associate Professor, Texas A&M University,
4041 Engineering Technology Building, 3131 Tamu, College
Station, TX, 77843-3131, United States, ,
Yu Fu
This study develops a two-stage Stochastic Integer Programming (SIP) model to
solve the online outpatient scheduling problem. The model considers different
types of patients and uncertain factors in system throughput, no-show,
cancellation and lateness. A modified L-shaped algorithm is designed to handle
the endogenous uncertainty brought by these factors and solve the SIP model.
The analysis method and solution algorithm can be applied to two-stage SIP
model with simple recourse function satisfying certain properties.
4 - A Stochastic Mixed Integer Programming Model For
Risk Minimization
Yiming Yao, Lawrence Livermore National Laboratory, 7000 East
Avenue, L-181, Livermore, CA, 94550-9234, United States,
, Vic Castillo, Andrew Mastin, Carol A Meyers,
Deepak Rajan
We present a two-stage stochastic mixed integer programming model that
minimizes enterprise risk subject to supply, demand, capacity and other
constraints, with the consideration of uncertainty in some parameter values. We
describe risk measurement and uncertainty characterization in the application
context. Finally, we describe the model’s implementation in the open source
optimization modeling language PYOMO/PYSP.
5 - Pricing Tax Return For Students That Opt-out From Using
School Bus
Hernan Andres Caceres Venegas, PhD Student, University at
Buffalo - SUNY, 342 Bell Hall, University at Buffalo, Buffalo, NY,
14260, United States, , Rajan Batta,
Qing He
School districts are often mandated to provide transportation but can encounter
ridership that varies between 22-72 percent. Consequently, buses run with
unused capacity over long routes. We explore the scenario where students are
compensated for giving up the option to ride a bus, in an effort to reduce the
overall cost of the system. Mathematical formulations for this problem are
developed and analyzed. Results from a case study along with algorithmic
computational results will be presented.
TB86
GIbson Board Room-Omni
Monte Carlo Methods for Multi-stage Decision
Making under Uncertainty
Sponsored: Artificial Intelligence
Sponsored Session
Chair: Michael Fu, University of Maryland,
1 - Back To The Future: Google Deep Mind, Alpha Go & Monte Carlo
Tree Search
Michael Fu, University of Maryland, College Park, MD, 20742,
United States,
In March 2016 in Seoul, Korea, Google DeepMind’s AlphaGo, a computer Go-
playing program, defeated the reigning human world champion Go player, a feat
far more impressive than previous computer programs victories in chess (Deep
Blue) and Jeopardy (Watson). Due to the sheer combinatorial nature of the
number of possibly game configurations, at the heart of all computer Go-playing
algorithms is Monte Carlo tree search based on an upper confidence bound (UCB)
algorithm that traces its roots back to an adaptive multi-stage sampling algorithm
for estimating the value function in finite-horizon Markov decision processes
(MDPs). We describe this algorithm and the main ideas behind AlphaGo.
2 - Cumulative Prospect Theory Meets Reinforcement Learning:
New Monte Carlo Algorithms
Cheng Jie, University of Maryland, Prashanth
L.A., Michael Fu, Marcus Steve, Csaba Szepesvari
We bring cumulative prospect theory (CPT) to a risk-sensitive reinforcement
learning (RL) setting and present Monte Carlo simulation-based algorithms for
both estimation and optimization. The estimation scheme uses the empirical
distribution to estimate the CPT-value of a random variable. The optimization
procedure is based on simultaneous perturbation stochastic approximation
(SPSA). Both theoretical convergence results and numerical experiments are
provided.
3 - Weighted Bandits Or: How Bandits Learn Distorted Values That
Are Not Expected
L. A. Prashanth, University of Maryland, College Park, MD, 20742,
United States, , Aditya Gopalan, Michael Fu,
Steve Marcus
We formulate a novel multi-armed bandit setup, where the arms’ reward
distributions are distorted by a weight function. The distortions are motivated by
models of human decision making that have been proposed to explain commonly
observed deviations from conventional expected value preferences We study two
representative problems in this setup: The classic K-armed bandit setting and the
linearly parameterized bandit setting. In both settings, we propose algorithms that
are inspired by UCB, incorporate reward distortions and exhibit sub-linear regret
assuming Holder-continuous weights. We provide empirical demonstrations of the
advantage due to using distortion-aware learning algorithms.
TB86