Technical Sessions – Monday July 11

3 - Improving Tsunami Warning Times for the Coastal Populations
of the Mediterranean Sea
Layna Groen, Mathematical Sciences, University of Technology,
Sydney, PO Box 123, 2007, Broadway, New South Wales,
Australia, Layna.Groen@uts.edu.au
Effective tsunami warning was given a higher priority on the world agenda following
the Boxing Day tsunami of 2004. International cooperation saw comprehensive
planning undertaken across the globe. Progress in implementation
has been made but in some regions it is unclear that performance targets would
be met by the improved infrastructure. The warning system serving the countries
of the Mediterranean is a case in point. We examine the effectiveness of
the current and planned Mediterranean warning systems and suggest how its
performance can be improved using set covering techniques.
� HD-10
Thursday, 15:30-17:00
Meeting Room 111
Stochastic Dynamic Optimisation and
Bayesian Methods
Stream: Stochastic Programming
Invited session
Chair: Riadh Zorgati, OSIRIS, EDF R&D, 1, Avenue du Géneral de
Gaulle, 92141, Clamart, IDF, France, riadh.zorgati@edf.fr
Chair: Pierre Girardeau, OSIRIS, EDF R&D, Place de la Division
Leclerc, 92140, Clamart, France, pierre.girardeau@ensta.org
1 - Probabilistic Optimization Applied to Inversion
Riadh Zorgati, OSIRIS, EDF R&D, 1, Avenue du Géneral de
Gaulle, 92141, Clamart, IDF, France, riadh.zorgati@edf.fr, Rene
Henrion, A. Moeller
We are dealing with the problem consisting in estimating an unknown vector x
which minimizes the residue between Ax and b when matrix A and vector b are
random. This problem, formulated as a bi-sided Chance-Constrained Programming,
is solved, in the linear case, by using rough conic approximations derived
from probability bounds. The approach is applied to electromagnetic inversion
and energy management. The results are compared with the exact solution obtained
in a multivariate setting with Gaussian distributions. The extension to
probabilistic least squares is discussed.
2 - A Bayesian Framework for Probabilistic Inversion
Nicolas Bousquet, EDF R&D, France, npg_bousquet@yahoo.fr,
Shuai Fu, Gilles Celeux, Mathieu Couplet
Having observations of a multidimensional random output of a time-consuming
computer code H, and knowing a set of deterministic environmental inputs, the
probabilistic inversion problem is estimating the distribution of the multidimensional
unobserved random input. Assuming expert knowledge can be elicited
about it, we consider a Bayesian statistical framework. A MCMC approach is
carried out to estimate its posterior predictive distribution, involving a kriging
emulator of H based on various static or sequential designs of experiments, then
illustrate its benefits on a hydraulical case-study.
3 - Dual Approximate Dynamic Programming Applied to
Chained Systems
Pierre Girardeau, OSIRIS, EDF R&D, 1 avenue du Général de
Gaulle, 92141, Clamart, France, pierre.girardeau@gmail.com,
Kengy Barty, Pierre Carpentier
We consider a dynamical system which can be influenced by exogenous noise.
In the Dynamic Programming framework, we look for policies as functions of a
state variable that characterizes the system. On some flower-shaped structured
systems, a Lagrangian dualization-type algorithm, called Dual Approximate
Dynamic Programming (DADP), has been successfully proposed and applied
to get round the curse of dimensionality.
We show how DADP may be applied to the more general setting of chained
subsystems and give interpretations about the approximate policies that we obtain.
IFORS 2011 - Melbourne HD-11
� HD-11
Thursday, 15:30-17:00
Meeting Room 112
Industry Applications - Airlines, Fishery
Stream: Integer Programming
Invited session
Chair: Bilge Atasoy, Transport and Mobility Laboratory, École
Polytechnique Fédérale de Lausanne (EPFL), CH-1015, Lausanne,
Switzerland, bilge.kucuk@epfl.ch
1 - Integrated Airline Schedule Planning with Supplydemand
Interactions
Bilge Atasoy, Transport and Mobility Laboratory, École
Polytechnique Fédérale de Lausanne (EPFL), CH-1015,
Lausanne, Switzerland, bilge.kucuk@epfl.ch, Matteo Salani,
Michel Bierlaire
In this study, we integrate a schedule design and fleet assignment model and
a demand model into an optimization problem to maximize the revenue of an
airline. The model considers spill and recapture effects to cope with capacity
restrictions. For the demand modeling, fares, fare classes, departure times and
number of stops are modeled by linear and nonlinear specifications. A heuristic
method is proposed to deal with the high complexity of the resulting mixed
integer nonlinear problem.
2 - Study on Improving Solution Regularity for Crew Pairing
Problems
Hanyu Gu, Constraint Technologies International, Australia,
Hanyu.Gu@constrainttechnologies.com, Ian Evans
When producing optimised crew pairings to cover airline schedules, many airlines
consider that regular pairings are easier to implement and manage, and
are to be preferred if there is limited cost impact. In this paper we propose a
column generation based solution approach to consider regularity, which is regarded
as the repeatability of pairings in the planning horizon, as well as cost.
The contributions of our method include the use of a fully dated model, an
improved k-shortest path pricing algorithm and comprehensive computational
results for a schedule from a large Asian airline.
3 - Column Generation for Fair-share Airline Crew Rostering
Ranga Muhandiramge, Caulfield School of IT, Monash
University, 900 Dandenong Rd, Caulfield East, 3145, Melbourne,
Victoria, Australia, ranga.muhandiramge@monash.edu
Airlines usually assign crew to tasks in two stages. In the first, flights are
grouped into pairings with the aim of minimizing costs. In the rostering stage,
these pairings are assigned to specific crew members. The aim of the second
stage is to maximize the perceived fairness of the roster, rather than cost minimization.
We apply an exact branch and price algorithm to a fair-share rostering problem
based on data from a large Asian airline. Different strategies to solve the
subproblem, which is a weight constrained shortest path problem on an acyclic
graph, are investigated.
4 - Optimizing Cage Net Use with Operations Research: A
Salmon Farm Pilot Project
Diego Delle Donne, Instituto de Ciencias, Universidad Nacional
de General Sarmiento, Juan María Gutierrez 1150, 1613, Los
Polvorines, Buenos Aires, Argentina, diegodd@gmail.com,
Francisco Cisternas, Guillermo Duran, Andrés Weintraub,
Cristian Polgatiz
Salmon farming in Chile constitutes one of the principal exporting sectors.
Salmon are cultivated in floating cages with nets to hold the fish during the
grow-out process. Maintenance of these nets is done at land facilities. In this
article we present a mixed-integer programming tool to optimize resource use,
improve planning and generate evaluations for analysis and decision-making
about repair and periodic changing of cage nets. The prototype was tested at
one of Chile’s largest salmon farmers and results showed a reduction in maintenance
costs of 16%, and many qualitative benefits.
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