2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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4 - Diameter-constrained Lambda-edge-connected
K-subgraph Problem
Yongying Zhou, University of Arizona,
In this talk, we study the diameter-constrained lambda-edge-connected
k-subgraph problem, or the DC (k, lambda)-subgraph problem. Besides the
requirements on the number of the vertices and edge connectivity, the subgraph
has a diameter limit. This problem is a generalization of (k, lambda)-subgraph
problem and diameter constrained minimum spanning tree. Commodity flow-
based and hop constrained formulations are established, which are both integer
programming (IP) formulation. Additionally, numerical experiments are
performed to compare all proposed IP formulation.
MD12
104B-MCC
Mixed Integer Programming Formulations
and Applications
Sponsored: Optimization, Integer and Discrete Optimization
Sponsored Session
Chair: Juan Pablo Vielma, Massachusetts Institute of Technology,
Cambridge, MA, United States,
1 - Small Independent Branching Formulations For Unions
Of V-polyhedra
Joey Huchette, Massachusetts Institute of Technology, Cambridge,
MA, United States, , Juan Pablo Vielma
We present a framework for constructing small, strong mixed-integer
formulations for disjunctive constraints. Our approach is a generalization of the
logarithmically-sized formulations of Vielma and Nemhauser for SOS2
constraints, and we offer a complete characterization of its expressive power. We
apply the framework to a variety of disjunctive constraints, producing novel,
small, and strong formulations for outer approximations of multilinear terms,
generalizations of special ordered sets, piecewise linear functions over a variety of
domains, and collision avoidance constraints.
2 - Embedding Formulations For Unions Of Convex Bodies
Juan Pablo Vielma, MIT,
In this talk we extend to the non-polyhedral setting a systematic procedure to
construct non-extended ideal formulations for unions of polyhedral introduced in
Vielma (2015). Using geometric tools from the study of Minkoswki sums we show
that the procedure can be used to recover and extend several special purpose
non-extended ideal formulations. We also illustrate how the tools can be used to
prove that no polynomially constrained non-extended ideal formulation can be
obtained for some simple convex quadratic sets.
3 - Winning Daily Fantasy Sports Hockey Contests Using Integer
Programming
Scott Hunter, MIT, , Juan Pablo Vielma,
Tauhid Zaman
We present an integer programming (IP) approach to winning daily fantasy sports
hockey contests which have top heavy payoff structures. Our approach
incorporates publicly available predictions into a series of IPs that compute
optimal lineups. We find that the produced lineups perform well in practice and
are able to come in first place in contests with thousands of entries. We also show
through simulations how the profit margin varies with various parameters. Our
approach can easily be extended to other sports, such as American football and
baseball.
4 - Smart Grids Observability Using Bilevel Programming
Claudia D’Ambrosio, Ecole Polytechnique, LIX CNRS (UMR7161),
Palaiseau, France,
Monitoring an electrical network is an important and challenging task. Phasor
measurement units (PMU) are devices that can be used for a state estimation of
this network. We consider a PMU placement problem and propose two new
approaches to model this problem, which take into account a propagation rule
based on Ohm’s and Kirchoff’s laws. First, we describe the natural binary linear
programming model based on an iterative observability process. Then, we remove
the iteration by reformulating its fixed point conditions to a bilevel program,
which we solve with a tailored cutting plane algorithm. Finally, we show
computational evidence of the effectiveness of our method.
MD13
104C-MCC
Project and Resource Planning
Sponsored: Optimization, Computational Optimization and Software
Sponsored Session
Chair: Haitao Li, Univ. of Missouri - St Louis, St Louis, MO,
United States,
Co-Chair: Cipriano A. Santos, Distinguished Technologist, HPe-IT
Center of Excellence, Virtual Office, Palo Alto, CA, 94304, United
States,
1 - An Optimization Approach To Workforce Planning In Professional
Service Firms
Vincent Hargaden, Assistant Professor, University College Dublin,
209 Engineering & Materials Science Centre, Belfield, Dublin 4,
Ireland, Jennifer K. Ryan, Amir Azaron
We develop a comprehensive mixed integer programming model for the
workforce planning process in professional service firms. We will present results
from the model which show the impact of skill mix, skill capability levels and
cross training on key performance metrics such as project completion rates, staff
utilization and profit. We show how extensions to our base model can incorporate
issues such as a rolling planning horizon approach and variable project start dates.
2 - Simulation -Optimization For Strategic Workforce Planning
Manuel Laguna, University of Colorado Boulder,
OptForce is a data analytics tool for workforce planning. We describe how
company data is used to build a simulation model of a workforce. This model is
then used for what-if analysis and optimization. The techniques associated with
this application of simulation-optimization are also discussed.
3 - Talent Optimization For The Knowledge Economy
Cipriano A. Santos, HP Enterprise,
Allocating the right talent for the right job at the right time, location, and cost is
critical for the operational efficiency of Professional Services Organizations. In this
talk we present a hierarchical planning approach for labor resources allocations
MD14
104D-MCC
IAAA and Sygenta Reprise
Sponsored: Analytics
Sponsored Session
Chair: Tarun Mohan Lal, Mayo Clinic, 1, Rochester, MN, 12345,
United States,
MD15
104E-MCC
Network Modeling and Inference
Sponsored: Artificial Intelligence
Sponsored Session
Chair: Adel Javanmard, Assistant Professor, University of Southern
California, Bridge Hall, 3670 Trousdale Parkway, Los Angeles, CA,
90089, United States,
1 - Co-clustering Of Non-smooth Graphons
David Sungjun Choi, Carnegie Mellon University,
Theory is becoming known for community detection and network clustering;
however, the results assume an idealized model that is unlikely to hold in many
settings. Here we consider exploratory co-clustering of a bipartite network whose
nodes are sampled from an arbitrary population. This is equivalent to assuming a
nonparametric generative model known as a graphon. We show that clusters
found in the data by any method will extend to the population, or equivalently
that the estimated blockmodel approximates a blocked version of the generative
graphon, with error bounded by n^{-1/2}. Analogous results are also shown for
degree-corrected and random dot product graph models.
MD12