Informs 2015

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INFORMS Philadelphia – 2015
377
WA11
11-Franklin 1, Marriott
Optimization Large Scale II
Contributed Session
Chair: Lukas Bach, SINTEF, Forskningsveien 1, Oslo, 0373, Norway,
1 - Assortment Planning for Configurable Products with
Consideration for Substitution
Ying Tang, Graduate Research Assistant, Wayne State University,
4815 Fourth Street, Detroit, MI, 48202, United States of America,
, Ratna Babu Chinnam, Alper Murat,
Joshua Lyon
We develop assortment planning models for vehicle programs of a large
automaker to support its strategic product planning efforts. We emphasize two
objectives: 1) Utilizing both non-parametric and parametric approaches for
characterizing demand. 2) Scalability of the models to support real-world
programs. We will also present results representative of a North American vehicle
program.
2 - Natural Gas System Operations and Expansion Planning
for Reliability
Conrado Borraz-sanchez, Associate Postdoctoral Researcher,
Los Alamos National Laboratory, 1927 22nd Street Apt. D,
Los Alamos, NM, 87544, United States of America,
Pascal Van Hentenryck,
Scott Backhaus, Russell Bent, Hassan Hijazi
We present a MINLP formulation to tackle natural gas network expansion
planning problems. Our model captures physical, operational, directionality and
on/off constraints. However, given its non-convexity, we propose a second-order
cone relaxation that proves to be highly effective on large-scale cases that include
existing Belgian and German networks. Comparisons against a piecewise
linearization approach also show the advantages of our approximation in terms of
its robustness and scalability.
3 - Adaptive Large Neighborhood Search using the
Graphics Processing Unit
Lukas Bach, SINTEF, Forskningsveien 1, Oslo, 0373, Norway,
, Geir Hasle, Christian Schulz
We investigate the efficiency of Adaptive Large Neighborhood Search on the
Graphics Processing Unit (GPU). We do this by implementing the algorithm for
the Distance-constrained Capacitated Vehicle Routing Problem (DCVRP), which
we benchmark towards a state of the art CPU implementation. The computational
power of the GPU in ordinary computers has increased significantly in recent
years. Therefore it is interesting to utilize this computing power. We perform tests
on well-known DCVRP instances.
WA12
12-Franklin 2, Marriott
Optimization Stochastic I
Contributed Session
Chair: Sebastian Maier, Imperial College London, South Kensington
Campus, London, SW7 2AZ, United Kingdom,
1 - A Hierarchical Markov Decision Process for Finding the Best
Replacement Policy of Fattening Pigs
Reza Pourmoayed, PhD Student, Aarhus University, Department
of Economics and Business, Fuglesangs Allé, Aarhus, 8210,
Denmark, Lars Relund Nielsen
We use a hierarchical Markov decision process to model the sequential decision
problem of replacing fattening pigs for slaughter. State of the system includes the
weight and the price information acquired by two statistical models based on a
Bayesian updating approach. Transition probabilities and rewards are calculated
using the statistical models and a simulation method, respectively. Numerical
examples are given to show the functionality of the proposed model.
2 - Quantile Optimization for Heavy-tailed Distributions using
Asymmetric Signum Functions
Ricardo Collado, Stevens Institute of Technology, Howe School of
Technology Management, Hoboken, NJ, United States of
America, , Jae Ho Kim, Warren Powell
We present an algorithm for computing the quantile of a continuous random
variable that does not require the existence of expectation or storing all of the
sample realizations. We use this to optimize the quantile of a random function
satisfying some strict monotonicity and differentiability properties. We apply this
to the problem of electricity trading in the presence of storage, where electricity
prices are known to be heavy-tailed with infinite variance.
3 - A Dynamic Size Sample Average Approximation for
Stochastic Optimization
Adindu Emelogu, Mississippi State University, 260 McCain
Building, Mississippi State, MS, 39762, United States of America,
Linkan Bian, Mohammad
Marufuzzaman
The Sample Average Approximation (SAA) is a method of solving stochastic
optimization problems by replacing the objective function with an approximation.
The size of the sample affects the convergence of the solution of the
approximation and the computation time. We propose an algorithm that
dynamically updates the sample size in SAA and ensures both convergence and
reasonable computation time. We apply our algorithm to a supply chain problem
in health care, and compare it with other algorithms.
4 - Risk-averse Stochastic Path Interdiction
Stephan Meisel, University of Muenster,
Department of Information Systems, Muenster, Germany,
Laura Priekule,
Ricardo Collado
We propose a new risk-averse approach to allocating security resources in a
network. Resources are allocated for blocking with high probability an attacker
that selects a path for traversing the network. The attacker is characterized by an
unknown probability distribution and resources are allocated based on beliefs
about the distribution. We formulate the problem as a linear program and use
coherent risk measures for getting solutions that are risk-averse with respect to
errors in the beliefs.
5 - Appraising Interdependent Physical and Digital Infrastructure
Investments: An Option Games Approach
Sebastian Maier, Imperial College London, South Kensington
Campus, London, SW7 2AZ, United Kingdom,
, David Gann, John Polak
We present a new option games-based appraisal framework for selecting a
portfolio of interdependent physical and digital infrastructure investments. We
have used this framework to formulate a multistage stochastic optimisation model
that combines the Least Squares Monte Carlo algorithm with the modelling of
infrastructure interdependencies. We investigate the sensitivity of the optimised
portfolio value and option exercise strategies to changes in competitor’s decisions
and strategic behaviour.
WA13
13-Franklin 3, Marriott
Stochastic Integer Programming Methods
and Applications
Sponsor: Optimization/Optimization Under Uncertainty
Sponsored Session
Chair: Lewis Ntaimo, Associate Professor, Texas A&M University, 3131
TAMU, College Station, TX, 77843, United States of America,
Co-Chair: Saravanan Venkatachalam, Texas A&M University,
1 - Scaling Scenario Decomposition Methods for 0-1
Stochastic Programming
Kevin Ryan, Georgia Institute of Technology,
, Deepak Rajan, Shabbir Ahmed
A recently proposed scenario decomposition algorithm for stochastic 0-1 programs
finds an optimal solution by evaluating and removing individual solutions
discovered by solving scenario subproblems. We develop techniques for applying
a parallel implementation of this algorithm to difficult problems with many first
stage variables and a moderate number of scenarios. Challenges include problem
symmetry and effective parallelization. Computational results from large scale
problems are presented.
2 - Robust Multicriteria Risk-averse Stochastic Programming
Simge Kucukyavuz, Associate Professor, The Ohio State
University, 210 Baker Systems Building 1971 Neil Ave,
Columbus, OH, United States of America,
Xiao Liu, Nilay Noyan
We study risk-averse models for multicriteria optimization problems under
uncertainty. We use a weighted sum-based scalarization and consider a set of
scalarization vectors to address the ambiguity and inconsistency in the relative
weights of each criterion. We introduce a model that optimizes the worst-case
multivariate CVaR and develop a finitely convergent algorithm for finite
probability spaces. Our computational study illustrates the effectiveness of the
proposed methods.
WA13