Technical Sessions – Monday July 11

HA-18 IFORS 2011 - Melbourne
Structural changes of an economy can occur as outcome of technological advance,
shocks and they can be traced along its development. Growth induces
social, political, financial system and sector and distribution structure changes,
and these contribute to further growth. In this paper preposition is that there
is a possibility to apply formal analytical models to follow structural changes
on sector level and there are patterns of developed countries expected to be
followed by developing ones. Particular attention will be paid to economy of
Serbia as a transitional country.
3 - Understanding Black Box: Knowledge Induction from
Mathematical Models
Jinhwa Kim, Business, Sogang University, 1 Shinsoo-Dong,
Mapo-Gu, Seoul, Korea, Republic Of, jinhwakim@sogang.ac.kr
This study explores approaches figuring what is inside black boxes such as
mathematical models. The experiment compares the performance of the random
dataset, RAA, elimination of redundant rules (ERR), and GA-RRA. In
order to verify the feasibility and effectiveness of the proposed algorithms, personal
credit rating dataset provided by a local bank in Seoul, Republic of Korea
is used in this study. The performance of these algorithms is compared to that
of the other algorithms.
4 - Single Period Mean-risk Portfolio Rebalancing Model
with a Hybrid Approach of the Stock Selection Phase
Cristinca Fulga, Department of Mathematics, Academy of
Economic Studies, Piata Romana 6, Sector 1, 010374, Bucharest,
Romania, fulga@csie.ase.ro
In this paper we are concerned with the portfolio optimization problem in the
mean-risk framework. We develop a portfolio selection method which takes
into consideration the recent positive evolution of the risky assets that are not
comprised in the portfolio available at the moment of the decision by combining
the Principal Component Analysis and the Analytical Hierarchy Process with
four key criteria: return, risk, liquidity and suitability. In our model, we rely on
a new quantile based risk measure and on a utility function which captures the
decision maker’s attitude towards risk.
� HA-18
Thursday, 9:00-10:30
Meeting Room 215
Applications of DEA in Banking and
Financial Institutions
Stream: Data Envelopment Analysis
Invited session
Chair: Jun Li, Southwestern University of Finance and Economics,
610074, Chengdu, China, sodoll@163.com
1 - Measuring Total Performance Score for Iranian Bank
Sector
Arash Aliakbari, Parto Novin Modiriat Iranian (Penco
Consulting Group ), Flat 4, No 22 , Unit 22 Separ St, Africa
Blvd., Tehran, Iran, Islamic Republic Of,
arashaliakbari@yahoo.com, Evangelia Pappa
Performance benchmarking is gradually taken to be fundamental for enhancing
productivity.Data Envelopment Analysis is a useful non-parametric technique
in evaluating efficiency performance of a sample of Iranian Banks, with multiple
inputs and outputs and monitor the gap between actual and targeted performance.In
our analysis, we will also incorporate Balance Scorecard approach,a
management tool which is structured along financial,marketing,operational and
strategic dimensions.
2 - Efficiency and Effectiveness of Australian Banks using
a Two-Stage Data Envelopment Analysis
Amir Moradi-Motlagh, Engineering and Industrial Sciences,
Swinburne University, 10 Henry Street, Sandringham, 3191,
Melbourne, VIC, Australia, moradimotlagh@yahoo.com, Amir
Abdekhodaee, Ali Salman Saleh, Mehran Motamed Ektesabi
This paper measures and analyses two aspects of the Australian banks performance
which are efficiency and effectiveness using a two-stage Data Envelopment
Analysis (DEA). Results indicate there is no apparent correlation between
the efficiency and effectiveness of the Australian banks. While large banks generally
perform better on the effectiveness, they have lower efficiency scores in
comparison with the medium sized banks. Therefore, this study could be a basis
for discovering strengths and weaknesses of the Australian banks in terms
of efficiency and effectiveness.
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3 - Efficiency and Malmquist Index of Banks in A Developing
Economy: The Case of China
...
Jun Li, Southwestern University of Finance and Economics,
610074, Chengdu, China, sodoll@163.com, Nan Zhu, Qing Wu,
Wenli Cheng
� HA-19
Thursday, 9:00-10:30
Meeting Room 216
Innovation in OR Education
Stream: Education and Operations Research
Invited session
Chair: Kellie Keeling, Business Information & Analytics, University
of Denver, Daniels College of Business, 2101 S. University Blvd,
80208-8952, Denver, CO, United States, Kellie.Keeling@du.edu
1 - OpenSolver - An Open Source Linear Optimizer for Excel
Andrew J Mason, Dept Engineering Science, University of
Auckland, Private Bag 92019, 1020, Auckland, New Zealand,
a.mason@auckland.ac.nz
We have developed OpenSolver as an open-source alternative to Excel’s inbuilt
Solver. OpenSolver works with Solver models, but uses the COIN-OR
CBC engine to solve much larger linear and integer programming problems,
making Excel’s optimization capabilities more accessible to OR practitioners.
OpenSolver also provides additional benefits including easy viewing of the underlying
formulation, and direct construction of the optimization model using
information provided on the spreadsheet. OpenSolver is available for download
at http://opensolver.org
2 - Linear Programming and Network Problems in Mathematics
Education: Middle and High School Experiences
Susana Colaco, Departamento de Ciências Matemáticas e
Naturais, Escola Suerior de Educação- Instituto politécnico de
Santarém, Compexo Andaluz, Apartado 131, 2000, Santarem,
Portugal, susana.colaco@ese.ipsantarem.pt, Margarida Pato,
Cecilia Rebelo
This talk is devoted to the implementation of mathematical tasks for the elementary,
middle and high school levels using linear programming and network
problems. Experiments comparing the same tasks in the middle and high
schools have been undertaken and the results will be analyzed on the basis of
the students representation, reasoning, and problem solving.
3 - Creating a Data Visualization Course
Kellie Keeling, Business Information & Analytics, University of
Denver, Daniels College of Business, 2101 S. University Blvd,
80208-8952, Denver, CO, United States, Kellie.Keeling@du.edu
With the large amount of data that is available, analysts continue to be challenged
to understand what is going on in our data. Advances in Business Analytics
software such as Tableau are allowing analysts to quickly and easily use
Data Visualization techniques to organize and highlight their data so that they
can determine what is happening and what might be causing it to happen. This
presentation will focus on Data Visualization as a course presented to undergraduate
and graduate students in a business school. A syllabus, course outline,
and suggestions for implementation will be shared.