Technical Sessions – Monday July 11

HC-14 IFORS 2011 - Melbourne
� HC-14
Thursday, 13:30-15:00
Meeting Room 207
New Approaches to Multi-objective
Optimization Problems I
Stream: Continuous and Non-Smooth Optimization
Invited session
Chair: Basak Akteke-Ozturk, Department of Industrial Engineering,
Middle East Technical University, 06531, Ankara, Turkey,
e114474@metu.edu.tr
Chair: Gerhard-Wilhelm Weber, Institute of Applied Mathematics,
Middle East Technical University, ODTÜ, 06531, Ankara, Turkey,
gweber@metu.edu.tr
1 - Combining Low Cost Procedures for the Improvement
Directions of Negative Curvature
Javier Cano, Rey Juan Carlos University, Spain,
javier.cano@urjc.es, Javier M. Moguerza, Francisco Prieto
In this work, we show how to combine procedures to improve directions of negative
curvature within an interior-point algorithm for constrained optimization.
These directions lead to a decrease in the iteration count of the procedure and
ensure convergence to second-order KKT points. The key feature is that the
combined directions have to be computed within the null subspace of the Jacobian
matrix of the constraints. To conclude, some numerical experiments are
presented, including real problems from the CUTEr collection, and simulated
problems with a controlled spectral structure.
2 - Multi-objective, Multimodal Transport Network Design,
using an Evolutionary Algorithm
Ties Brands, Centre for Transport Studies, University of Twente,
Postbus 217, 7500AE, Enschede, Netherlands,
t.brands@utwente.nl, Eric van Berkum
The paper is about the design of a multimodal transport network that is optimal
with respect to multiple objectives. We focus on transfer facilities and service
lines as decision variables. For the multi-objective optimization a bi-level approach
is chosen which includes a fast multimodal network assignment module
and an evolutionary algorithm. This results in a set of Pareto-optimal solutions
which provides insight in the interdependencies between objective functions.
After applying a pruning method on the Pareto set a concise overview of
promising networks and their properties is provided.
3 - The Hyperbolic Smoothing Approach for Continuous
Hub-and-Spoke Location in Multiple Assignment Networks
Adilson Elias Xavier, Graduate School of Systems Engineering
and Computer Sciences, Federal University of Rio de Janeiro,
P.O. Box 68511, Ilha do Fundão - Centro Tecnologia - H319,
21941-972, Rio de Janeiro, RJ, Brazil, adilson@cos.ufrj.br,
Claudio Gesteira, Henrique Pacca Loureiro Luna
This paper considers the problem of finding the least expensive hub locations,
given demands between pairs of cities and transportation costs. The problem
leads to a formulation that is strongly non-differentiable. The proposed method
overcomes this difficulty with a smoothing strategy. The final solution is obtained
by solving a sequence of differentiable unconstrained optimization subproblems.
The most important feature of the methodology is the low dimension
of the subproblems. The efficiency of the method is shown through a set of
computational experiments with large problems.
� HC-15
Thursday, 13:30-15:00
Meeting Room 208
OR Practice: Practical Aids for the Military
Stream: Military, Defense and Security Applications
Invited session
Chair: Maria John, Defence Science & Technology Organisation, SA,
Australia, maria.john@dsto.defence.gov.au
94
1 - An Operations Research Support to the Basis of Provisioning
in an Army Project
Maria John, Defence Science & Technology Organisation, SA,
Australia, maria.john@dsto.defence.gov.au, Richard Egudo,
Nicholas Kempt, Nikoleta Tomecko, Natasha Weaver, David
Sanderson
The presentation outlines the development of a decision support tool for the
acquisition of an Army capability. The capability representation consists of
two key dimensions: role requirements and classes of systems. A matrix is
formulated describing the suitability of classes of systems in meeting the role
requirements. Feasible options are generated by an integer programming model
that incorporates the suitability matrix, role priorities, system costs and budget.
The results of the model enable an Options Working Group to recommend a set
of feasible options for further development by Army.
2 - Multi-Agency Security Risk Management
Svetoslav Gaidow, LOD, DSTO, DSTO-LOD-205L, West
Avenue, 5111, Edinburgh, South Australia, Australia,
svetoslav.gaidow@dsto.defence.gov.au, Fred Bowden, Richard
Nunes-Vaz
A governance framework is proposed for recursive application of risk management
in complex, multi-agency security environments. A transactional view
is adopted in describing the logic of each phase in the process, to ensure the
integrity and coordination of each agency’s contribution to strategic objectives.
Transactions between phases and agencies may be represented using both
generic and tailored devices, which also have direct utility in focusing agency
interactions.
3 - Factors Effecting Reach Back Support to Decision Making
Processes
Tim McKay, JOD, DSTO, PO Box 1500, 5111, Edinburgh, SA,
Australia, tim.mckay@dsto.defence.gov.au, Goran Mihelcic,
Stefan Pickl, Christian Wittmann
Having expertise at hand when it is needed is crucial in several military as well
as civilian crises scenarios. Quite often, the expertise needed is globally distributed
or the sending of experts into the problem area may be too dangerous
or time consuming. By connecting the team within the crisis area to a remote
organizational unit — the "reach back’ - this situation can be tackled by providing
remote expertise. The challenges and enabling factors of this distributed
decision support cooperation are discussed in this paper.
� HC-16
Thursday, 13:30-15:00
Meeting Room 209
OR for Hospital and Regional Health
Services II
Stream: Health Care Applications
Invited session
Chair: Kristian Rotaru, Monash University, 1 Douglas Ct, 3074,
Thomastown, Victoria, Kristian.Rotaru@buseco.monash.edu.au
1 - Bed Management in Critical Care Unit
Izabela Komenda, Mathematics, Cardiff University,
Senghennydd Road, CF24 4AG, Cardiff, United Kingdom,
komendai@cardiff.ac.uk, Jeff Griffiths, Vincent Knight
Hospital managers are faced with many problems relating to bed management.
These include coping with the uncertainty in daily demand for the services,
whilst also attempting to increase the flow levels of patients through the system.
In this paper we propose a mathematical model which shows how improvements
in bed management may be achieved by distinguishing between various
categories of patients. Previous literature in this field is concerned mainly with
steady-state conditions, whereas in reality activities in virtually all hospital environments
are very much time-dependent.