2016 INFORMS Annual Meeting Program

368 / 561

INFORMS Nashville – 2016
368
2 - A Hybrid Approach To Centralized Drug Inventory Management In
A Chain Of Pharmacy Stores
Tom Brady, Purdue University, 1401 S US Hwy 421, Westville, IN,
46391, United States,
Inventory analysis has long been a fundamental component of operations
research, decision analysis, and operations management. In this paper, we discuss
a hybrid approach of inventory policy to pharmaceuticals applied to small
pharmacy chain. Decentralized inventory management policy had been in place
as the chain grew where the individual store managers were responsible for
inventory decisions. After an analysis of the inventory from the chain perspective,
it was theorized that by moving certain inventory policy decisions to the chain
level rather than the store level could result in potential inventory savings while
maintain customer service targets.
3 - Managing Inventory Of Perishable Products at Multiple Locations
Fang Liu, Assistant Professor, Nanyang Technological University,
S3-B2a-13, 50 Nanyang Avenue, Singapore, 639798, Singapore,
, Yun Fong Lim
A retailer selling multiple perishable products with random demands over a single
season. The warehouses, each with a limited storage capacity, have different store
and retrieve costs. Before demand realizes, the products are stored to each
warehouse and after demand realizes products are retrieved from the
warehouses. We develop a two-dimension table to determine under optimality
which warehouses are non-empty, and among the non-empty warehouses, the
products are stored following a nested assortment structure. In the retrieval stage,
it is optimal to retrieve a product from a warehouse with the lowest retrieve cost
that contains the product.
4 - Solving Stochastic Inventory Problems By Integer Programming
Reha Uzsoy, North Carolina State University, Dept. of Industrial &
Systems Engg, 300 Daniels Hall Camps Box 7906, Raleigh, NC,
27695-7906, United States, , Jaap Arts,
Ton de Kok, Seza Orcun
We propose binary integer programming models to approximately solve finite-
horizon stochastic inventory problems with stochastic demand. The models
proceed by discretizing the PDF of the demand in each period in a manner
emulating numerical integration. The resulting integer programs have a
consecutive ones property which provides additional computational efficiency.
Cost minimizing service levels are endogenously determined. Computational
results are reported for a variety of single-stage stochastic inventory models.
WA16
105A-MCC
Large-scale Stochastic Mixed-integer Programs
Sponsored: Optimization, Optimization Under Uncertainty
Sponsored Session
Chair: Gabriel Lopez Zenarosa, University of Pittsburgh, 3700 O’Hara
Street, Benedum Hall 1048, Pittsburgh, PA, 15261, United States,
1 - PH-BAB: A Progressive Hedging Based Branch And
Bound Algorithm
Semih Atakan, PhD Student, University of Southern California,
Los Angeles, CA, United States, , Suvrajeet Sen
Progressive Hedging (PH) is a well known algorithm for solving multi-stage
stochastic convex optimization problems. Most previous extensions of PH for
stochastic mixed-integer programs (SMIPs) have been implemented without
convergence guarantees. In this talk, we present a new framework that shows
how PH can be utilized while guaranteeing convergence to globally optimal
solutions of SMIPs. We demonstrate the efficacy of the proposed framework
through computational experiments.
2 - New Approaches For The Stochastic Network
Interdiction Problem
Eli Towle, University of Wisconsin-Madison,
Jim Luestke
We investigate methods to solve the maximum-reliability stochastic network
interdiction problem (SNIP). To begin, we introduce a novel reformulation of the
SNIP extensive formulation. We then propose a path-based formulation of the
SNIP. We present cuts for this new formulation which are dependent on the
structure of the given interdicted arc probabilities. To solve this path-based SNIP
formulation, we implement a branch-and-cut (BC) algorithm. Computational
results demonstrate an improvement of this BC algorithm over a traditional
Benders decomposition.
3 - Two-stage Stochastic Programming For Influence Maximization
Hao-Hsiang Wu, Ohio State University, Columbus, OH, 43202,
United States, Simge Kucukyavuz
Influence maximization problem is to find top-k influential nodes in a social
network. Kempe. et al propose the greedy algorithm as a heuristic to solve the
linear threshold and independent cascade models for influence maximization. By
using the greedy algorithm, only 63% optimal value can be guaranteed. In this
project, we formulate influence maximization problem as a two-stage stochastic
program, and use a delayed constraint generation algorithm for its exact solution.
Our algorithm exploits the submodularity of the influence spread function. We
demonstrate the performance of our algorithm on large-scale real-world datasets.
4 - Solving Large-scale Stochastic Programs Using Generalized
Value Function
Onur Tavaslioglu, PhD Student, University of Pittsburgh,
Pittsburgh, PA, United States, Andrew J Schaefer,
Oleg A Prokopyev
This work considers two-stage mixed integer programs with discretely distributed
stochastic right-hand sides and objective functions. We present an equivalent
superadditive dual formulation that uses the value functions of both stages. We
introduce the generalized value function of a mixed-integer program
simultaneously parametrized by its objective function and right-hand side. We
describe fundamental properties of the generalized value function and three
algorithms to calculate it. Then, we present a global branch-and-bound algorithm
for solving one stage pure integer and one stage mixed integer program. We
conclude with computational results.
WA17
105B-MCC
Convex and Conic Relaxations in Machine Learning
Sponsored: Optimization, Nonlinear Programming
Sponsored Session
Chair: Amir Ali Ahmadi, Princeton University, 329 Sherrerd Hall,
Princeton, NJ, 08540, United States,
Co-Chair: Georgina Hall, Princeton University, Sherrerd Hall, Princeton,
NJ, 08544, United States,
1 - A New Perspective On Boosting In Linear Regression Via
Subgradient Optimization And Relatives
Rahul Mazumder, MIT Sloan School of Management,
Boosting is one of the most powerful and popular tools in machine learning/
statistics that is widely used in practice. They work extremely well in a variety of
applications. However little is known about many of the statistical and
computational properties of the algorithm, and in particular their interplay. We
analyze boosting algorithms in linear regression from the perspective modern
first-order methods in convex optimization. We derive novel comprehensive
computational guarantees for several boosting algorithms, which provide a precise
theoretical description of the amount of data-fidelity and regularization imparted
by running a boosting algorithm.
2 - Making Sketchy Decisions: Semidefinite Programming With
Optimal Storage
Madeleine Udell, Cornell University, Ithaca, NY, United States,
, Joel Tropp, Alp Yurtsever, Volkan Cevher
Is it possible to solve an optimization problem using far less memory than the
natural size of the decision variable? In this talk, we propose an affirmative
answer to this question when both the problem data and solution have a concise
representation. We present an algorithm for provably solving many semidefinite
programming problems whose natural size is O(n^2) using no more than O(n)
memory.
WA16