Technical Sessions – Monday July 11

203, 1200-781, Lisboa, Portugal, cmourao@iseg.utl.pt, Enrique
Benavent, Angel Corberan, Luis Gouveia, Leonor S.Pinto
The Profitable Mixed Capacitated Arc Routing Problem is a generalization of
the Profitable Arc Tour Problem. Tasks may be either mandatory or optional.
Each task has an associated demand, a profit, a deadheading cost and a traversal
time. The objective is to find a set of tours that maximize the total net profit,
respecting a maximal tour length and the vehicles capacity. The profit over a
link is available only once, when the service is performed. We present compact
flow based models for the PMCARP. Valid inequalities are also presented. The
quality of the models is tested and analysed.
3 - Optimization of Bus Schedules in Event Tourism using
Mixed-integer Linear Programming
Gianluca Brandinu, Department of Business Administration,
University of Bern, Schuetzenmattstrasse 14, 3012, Bern,
Switzerland, gianluca.brandinu@pqm.unibe.ch, Norbert
Trautmann
We study a bus tour of Bollywood-movie locations in Switzerland. The tour
operator runs up to five busses per day; however, two or more busses cannot
stay at the same location simultaneously. The planning problem consists in
computing a feasible schedule for each bus such that a lexicographic combination
of the total waiting and the total travel time is minimized. We formulate
this problem as an MILP, and we report on numerical results obtained with the
Gurobi Solver. We demonstrate how the computational effort can be reduced
by introducing symmetry-breaking constraints.
4 - Optimized Long-term Freight Infrastructure Planning
Simon Dunstall, Mathematics, Informatics and Statistics,
CSIRO, Private Bag 33, 3169, South Clayton, Victoria, Australia,
Simon.Dunstall@csiro.au, Andreas Ernst, Kim Levy, Stuart
Woodman, Olena Gavriliouk, Andrew Higgins, Martin
Savelsbergh, Gaurav Singh
The improvement of transport infrastructure for minerals freight continues in
Australia, particularly in Queensland where the government and CSIRO have
constructed a freight network optimization system. This system is known as
the "Infrastructure Futures Analysis Platform", has a GIS interface and optimizes
the capacity of roads, railways and facilities yearly over a 25 year horizon
while satisfying freight demand and various practical constraints. We describe
the mathematical model and our experience in working with GIS packages and
freight transport policymakers.
� MC-07
Monday, 16:00-17:30
Meeting Room 106
OR Software
Stream: OR software
Invited session
Chair: Pim Beers, Paragon Decision Technology, Julianastraat 30,
2012ES, Haarlem, Netherlands, p.beers@aimms.com
1 - Tackling Large-scale Optimization Problems within a
Python-based Modeling Environment (Pyomo)
William Hart, Data Analysis & Informatics, Sandia National
Laboratories, P.O. Box 5800, 87185, Albuquerque, NM, United
States, wehart@sandia.gov, John Siirola, Jean-Paul Watson
The Pyomo modeling package can be used to formulate optimization models
natively within the Python scripting language. Pyomo is an open-source modeling
language that is being actively developed to support COIN-OR users in a
variety of application areas. This talk will highlight recent developments in Pyomo
and related Coopr packages, focusing on the overall speed and scalability
of the modeling environment for large-scale optimization problems.
2 - Attacking Hard Mixed-Integer Optimization Problems
Using an Algebraic Modeling Language
Robert Fourer, AMPL Optimization LLC, 2521 Asbury Avenue,
60201-2308, Evanston, IL, United States, 4er@ampl.com
IFORS 2011 - Melbourne MC-08
There are many tricks for formulating complex optimization models by use of
integer variables, but what’s to be done when even the most advanced solvers
can’t produce results in reasonable time? Substantial improvements in performance
can often be achieved through carefully focused troubleshooting and experimentation.
And sometimes, much better results can be achieved by "cheating"
a bit on the formulation. A series of case studies illustrate that although a
few general principles can offer guidance, much trial and error is involved, for
which purpose a flexible modeling language is ideal.
3 - AIMMS Modelling System - Demonstration
Pim Beers, Paragon Decision Technology, Julianastraat 30,
2012ES, Haarlem, Netherlands, p.beers@aimms.com
AIMMS is an advanced modeling system for building optimization-based decision
support applications. It is used by leading companies to support decision
making and by universities for teaching operations research courses and for research
projects. The talk will consist of a basic introduction of the modeling
system AIMMS. We will give a global overview of the model representation,
the graphical user interface, the solver and algorithmic capabilities. The presentation
will contain a demonstration on how to build a small transport model
in AIMMS.
� MC-08
Monday, 16:00-17:30
Meeting Room 107
Computational Methods in Biomolecular
and Phylogenetic Analyses
Stream: Bioinformatics
Invited session
Chair: Ming-Ying Leung, The University of Texas at El Paso, TX
79968-0514, El Paso, United States, mleung@utep.edu
1 - An Optimization Approach to Efficient Sampling over
Phylogenetic Trees
Russell Schwartz, Carnegie Mellon University, United States,
russells@andrew.cmu.edu, Navodit Misra, Guy Blelloch, R Ravi
Phylogenetics, or the inference of evolutionary trees, is commonly solved by
Monte Carlo sampling over tree topologies, but prevailing methods depend on
heuristic mixing time bounds that provide no guarantees of adequate mixing.
We present a new strategy for generating provably well mixed samples efficiently
in practice for important special cases of the problem. Our method
reformulates the problem to create a rapidly mixing chain by performing a theoretically
hard but practically efficient optimization on each sampling step.
2 - Using Massively Parallel Ssequencing Data in Family
Studies
Melanie Bahlo, The Walter and Eliza Hall Institute of Medical
Research, 3052, Parkville, VIC, Australia, bahlo@wehi.edu.au
Massively parallel sequencing (MPS) data is making a strong impact in the
field of disease gene identification, particularly in single gene disorders. We
describe some of our recent work in extending methods using this new technology
with two examples: (1) estimation of relatedness of individuals using
RNA-seq data and (2) application of traditional statistical mapping techniques
to MPS data to more effectively filter likely causal variants.
3 - Comparative Studies in Biological Networks via Graphbased
Features
Kwok Pui Choi, National University of Singapore, 119077,
Singapore, Singapore, stackp@nus.edu.sg
Biomolecules orchestrate higher cellular functions. Recent breakthroughs in
biotechnology have generated much information on biomolecular interactions
in a genomic scale. Mathematical and computational approaches prove to be
indispensible in providing deeper insights into these networks. Various graphbased
measures, including the well known node degree distribution and clustering
coefficient, have been introduced. We will explore the relationships among
these measures and how they can be collectively applied to assess which evolutionary
network model best describe a given biological network.
4 - A Data Analysis System for iTRAQ
Penghao Wang, School of Mathematics and Statistics, University
of Sydney, 2006, Sydney, NSW, Australia,
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