Technical Sessions – Monday July 11

4 - A Robust Planning Approach for Final Assembly in Special
Purpose Machinery
Christian Gahm, Chair Of Business Administration, Production
& Supply Chain Management, Augsburg University,
Universitätsstraße 16, 86159, Augsburg, Germany,
christian.gahm@wiwi.uni-augsburg.de, Bastian Dünnwald
Planning the final assembly of special purpose machinery is marked by high
uncertainty. Against this background we developed an integrated optimizationsimulation
planning approach calculating a robust production plan, which minimizes
lead times (WIP) and assures deadlines. The approach comprises a planning
method that considers uncertainty by correction factors and an algorithm
to solve the hybrid-flow-shop problem with variable-intensity and preemptive
tasks. The implemented DSS focuses usability as well as adaptability and its
application in an aerospace company shows impressive results.
� FB-13
Friday, 13:15-14:45
Meeting Room 206
Mathematical Programming IV
Stream: Continuous and Non-Smooth Optimization
Invited session
Chair: Xiaoqi Yang, Department of Applied Mathematics, The Hong
Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong,
mayangxq@polyu.edu.hk
Chair: Regina Burachik, School of Mathematics and Statistics,
University of South Australia, Mawson Lakes, 5095, Adelaide, South
Australia, Australia, regina.burachik@unisa.edu.au
1 - Continued Iterations on Interior Point Methods
Aurelio Oliveira, Computational & Applied Mathematics, State
University Of Campinas, DMA IMECC Unicamp, C. P. 6065,
13081-970, Campinas, SP, Brazil, aurelio@ime.unicamp.br,
Lilian Berti
The search directions on interior point methods are projected along the blocking
constraint in order to continue the iteration. The process can be repeated
while the projected direction is a good one in some measure. Since the such direction
is as easy to compute as the corrector one, the approach can contribute
to speed up convergence by reducing the total number of iterations. Numerical
experiments show that the approach is promising when applied at the last
interior point methods iterations.
2 - Implementation of a block-decomposition algorithm for
solving large-scale conic semidefinite programming
problems
Camilo Ortiz, School of Industrial & Systems Engineering,
Georgia Institute of Technology, 765 Ferst Drive, NW,
30332-0205, Atlanta, Georgia, United States,
camiort@gatech.edu, Renato D.C. Monteiro, Benar F. Svaiter
We consider block-decomposition first-order methods for solving large-scale
conic semidefinite programming problems. Several ingredients are introduced
to speed-up the method in its pure form such as: an aggressive choice of stepsize
for performing the extragradient step; and the use of scaled inner products
in the primal and dual spaces. Finally, we present computational results showing
that our method outperforms the two most competitive codes for large-scale
semidefinite programs, namely: the boundary point method by Povh et al. and
the Newton-CG augmented Lagrangian method by Zhao et al.
3 - On a Polynomial Merit Function for Interior Point Methods
Luiz Rafael Santos, Computational & Applied Mathematics,
University of Campinas, Rua Sérgio Buarque de Holanda, 651,
Cidade Universitária, 13083-859, Campinas, São Paulo, Brazil,
lrsantos@ime.unicamp.br, Fernado Villas-Bôas, Aurelio
Oliveira, Clovis Perin
Predictor-corrector type interior point methods are largely used to solve linear
programs. In this context, we develop a polynomial merit function that
arises from predicting the next residue of each iterate and that depends on three
variables: a centralizer weight, a corrector weight, and a step size. We also generalize
Gondzio’s symmetric neighborhood, and the merit function is subjected
to this neighborhood. A constrained global optimization problem results from
this method and its solution leads to a good direction. Numerical experiments
and comparisons to PCx are performed.
IFORS 2011 - Melbourne FB-14
4 - Semi-infinite Program with Infinitely Many Conic Constraints:
Optimality Condition and Globally Convergent
Algorithm
Shunsuke Hayashi, Graduate School of Informatics, Kyoto
University, Yoshida-Honmachi, Sakyo-Ku, 606-8501, Kyoto,
Japan, shunhaya@amp.i.kyoto-u.ac.jp, Takayuki Okuno, Masao
Fukushima
We focus on the semi-infinite conic program (SICP), which is to minimize a
convex function subject to infinitely many conic constraints. We show that,
under Robinson’s constraint qualification, an optimum of the SICP satisfies the
KKT conditions that can be represented only with a finite subset of the conic
constraints. We also introduce an exchange type algorithm combined with a
regularization technique, and show that it has global convergence. We also
give some numerical results to see the efficiency of the proposed algorithm.
� FB-14
Friday, 13:15-14:45
Meeting Room 207
Optimization Modeling and Equilibrium
Problems II
Stream: Continuous and Non-Smooth Optimization
Invited session
Chair: Larry LeBlanc, Owen Graduate School of Management,
Vanderbilt University, 401 21st Avenue South, 37203, Nashville, Tn,
United States, larry.leblanc@owen.vanderbilt.edu
Chair: Dominik Dorsch, Dept. Mathematics, RWTH Aachen
University, Templergraben 55, 52056, Aachen, NRW, Germany,
dorsch@mathc.rwth-aachen.de
1 - Implementing Optimization Models: Organization Capability
and Analytical Needs
Larry LeBlanc, Owen Graduate School of Management,
Vanderbilt University, 401 21st Avenue South, 37203, Nashville,
Tn, United States, larry.leblanc@owen.vanderbilt.edu, Thomas
Grossman
We explain why recent technological advances have increased the value optimization
for analytics and present approaches for bringing optimization to
bear. These are applying optimization to a traditional manually-constructed
spreadsheet, using Excel’s built-in VBA prior to applying optimization, and a
mathematical approach using Excel for input/output to special-purpose algebraic
modeling language. We explain the relative strengths and weaknesses
of each approach and guide analysts in selecting the approach that takes into
consideration both organizational capability and analytical needs.
2 - Integrated Framework for Fuel Reduction and Fire Suppression
Resource Allocation
James Minas, Mathematical and Geospatial Sciences
Department, RMIT University, GPO Box 2476V, 3001,
Melbourne, VIC, Australia, james.minas@rmit.edu.au, John
Hearne
Wildfire-related destruction is a global problem that appears to be worsening.
Wildfire management involves a complex mix of interrelated components including
fuel management, fire weather forecasting, fire behaviour modelling,
values-at-risk determination and fire suppression. We propose a mathematical
programming model that provides an integrated risk-based framework for fuel
management and fire suppression resource allocation.
3 - An Intelligent Model Using Excel Spread Sheet in a
Manufacturing System
Kanthen K Harikrishnan, School of Applied Mathematics
Faculty of Enggineering, Nottingham University Malaysia
Campus, Jalan Broga„ 43500, Semenyih, Selangor, Malaysia,
Harikrishnan.KK@nottingham.edu.my, Ho Kok Hoe, Kanesan
Muthusamy
The research integrates the combination of mathematical modelling and intelligent
modelling concept to represent a manufacturing system in the Excel spread
sheet interface. The mathematical modelling uses two types of approaches,
which use IE variables and the development of mathematical theorem using
deduction methodology from the dynamic manufacturing system. Mathematical
language and mathematical reasoning technique through programming in
spread sheet are used to build an intelligent modelling system which optimises
targeted inventory level in order to response without human involvement.
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