2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
154
MB18
106A-MCC
Recent Advances in Theory and Applications of IPCO
Sponsored: Optimization, Integer and Discrete Optimization
Sponsored Session
Chair: Manish Bansal, Northwestern University, C234 Technological
Institute, 2145 Sheridan Road, Evanston, IL, 60202, United States,
1 - Decomposition For Loosely Coupled Mixed-integer Programs:
A Multiobjective Perspective
Merve Bodur, Georgia Institute of Technology,
, Natashia Boland, Shabbir Ahmed,
George L Nemhauser
We consider loosely coupled mixed-integer programs (MIPs), that consist of
(possibly a large number of) interrelated subsystems and a small number of
constraints, which link blocks of variables that correspond to different subsystems.
Motivated by recent developments in multi-objective programming (MOP), we
develop a MOP-based branch-and-price algorithm to solve loosely coupled MIPs.
We discuss the similarities and differences of our algorithm with the traditional
branch-and-price algorithm. Also, we present computational results on instances
with knapsack structure in the subsystems.
2 - Maximum Demand Rectangular Location Problem
Manish Bansal, Northwestern University, Evanston, IL, United
States, , Kiavash Kianfar
We introduce a new generalization of the classical planar maximum coverage
location problem by positioning a given number of rectangular service zones (SZs)
on the 2-D plane to cover a set of existing (possibly overlapping) rectangular
demand zones such that the total covered demand is maximized. We refer to this
problem as Maximum Demand Rectangular Location Problem (MDRLP) which
also has application in camera-frame selection for telerobotics. We present an
improved algorithm for the single-SZ MDRLP, which is at least two times faster
than the existing exact algorithm. We then provide theoretical properties for
multi-SZ MDRLP and an exact algorithm to solve it along with our computational
results.
3 - Cutting Planes fromMultiple-term Disjunctions
Egon Balas, Carnegie Mellon University,
(Less than 600 characters excluding spaces): Lift-and-project cuts from split
disjunctions have their counterpart as intersection cuts from a (feasible or
infeasible) LP tableau, and thus can be generated by pivoting in the latter. This
correspondence breaks down in the case of general disjunctions: here the bases of
the CGLP and associated L&P cuts can be either regular or irregular. Irregular cuts
do not correspond to intersection cuts and cannot be obtained by pivoting in the
LP tableau; they tend to be more numerous and stronger than regular cuts. Some
irregular L&P cuts can be generated without recourse to a higher dimensional
CGLP, as generalized intersection cuts from the disjunction underlying the L&P
cut.
MB19
106B-MCC
Models and Methods for Large-Scale
Mixed-Integer Optimization
Sponsored: Computing
Sponsored Session
Chair: Simge Kucukyavuz, Ohio State University, Ohio State University,
Columbus, OH, United States,
1 - Two-stage Stochastic Programming Models Under Multivariate
Risk Constraints
Merve Merakli, Ohio State Universtity, Columbus, OH,
United States, , Simge Kucukyavuz,
Nilay Noyan
In this study, we consider multicriteria risk-averse two-stage stochastic
programming problems. The aim is to find the best decision for which the
associated random outcome vector of interest is preferable to a specified
benchmark with respect to the multivariate polyhedral conditional value-at-risk
relation. In this case, classical decomposition methods can not be used due to
complicating risk constraints. We propose an exact solution algorithm based on
Benders decomposition and show its convergence. Computational experiments
are performed on a disaster relief network design problem.
2 - Chance-constrained Stochastic Programming Under Variable
Reliability Levels With An Application To Humanitarian Relief
Network Design
Özgün Elçi, Sabanci University, Istanbul, 34956, Turkey,
, Nilay Noyan, Kerem Bulbul
A recently introduced class of models treats reliability levels associated with
chance constraints as decision variables and trades off the actual cost against the
cost of the selected reliability levels. Leveraging recent methodological advances
for solving chance-constrained linear programs with fixed reliability levels, we
develop strong MIP formulations for this new variant with variable reliability
levels. In addition, we introduce an alternate cost function type associated with
the reliability levels which requires capturing the value-at-risk associated with a
variable reliability level. We apply the proposed modeling approach to a new
humanitarian relief network design problem.
3 - Bilevel Risk Averse Formulations Of Stochastic
Programming Problems
Deniz Eskandani, Rutgers University, 100 Rockafeller Rd,
Piscataway, NJ, 08854, United States,
Jonathan Eckstein
We describe a bilevel programming technique to time-consistently formulate 3-
stage stochastic programs without using nested risk measures. For some classes of
applications, we empirically demonstrate that its behavior can be dramatically
different from standard formulations.
4 - Irreducible Infeasible Subsystem Decomposition For Stochastic
Integer Programs With Probabilistic Constraints
Lewis Ntaimo, Associate Professor, Texas A&M University, College
Station, TX, United States,
Bernardo Pagnoncelli
Probabilistically constrained stochastic integer programs (PC-SIPs) are very
challenging to solve and linear programming (LP) provides very weak bounds on
the optimal value. This work considers a decomposition approach using
irreducible infeasible subsystem (IIS) inequalities for strengthening the LP-
relaxation of PC-SIPs. Preliminary computational results will be presented.
MB20
106C-MCC
Research and Teaching Opportunities in
Project Management
Invited: Tutorial
Invited Session
Chair: Nicholas G Hall, Ohio State University, 658 Fisher Hall, 2100 Neil
Avenue, Columbus, OH, 43210-1144, United States,
1 - Research And Teaching Opportunities inProject Management
Nicholas G Hall, Ohio State University, 658 Fisher Hall,
2100 Neil Avenue, Columbus, OH, 43210-1144, United States,
One-fifth of the world’s economic activity, with an annual value of $12 trillion, is
organized using the business process of project management. This process has
exhibited dramatic growth in business interest in recent years, with a greater than
1000% increase in Project Management Institute membership since 1996.
Contributing to this growth are many new applications of project management.
These include IT implementations, research and development, software
development, corporate change management, and new product and service
development. However, the very different characteristics of these modern projects
present new challenges. The partial resolution of these challenges within project
management practice over the last 20 years defines numerous interesting
opportunities for academic researchers. These research opportunities make use of
a remarkably broad range of methodologies, including robust optimization,
cooperative and noncooperative game theory, nonlinear optimization, predictive
analytics, empirical studies, and behavioral modeling. Furthermore, the $4.5
trillion that is annually at risk from a shortage of skilled project managers, and the
15.7 million new jobs in project management expected by 2020, provide great
opportunities for contributions to project management education. These
educational opportunities include the integration of case studies, analytics
challenges, online simulations, in-class games, self-assessment exercises, videos,
and guest speaker presentations, which together form an appealing course for
both business and engineering schools.
MB18