Technical Sessions – Monday July 11

Friday, 15:15-16:45
� FC-01
Friday, 15:15-16:45
Plenary Hall 3
OR, Energy, and Africa
Stream: OR Applications in Energy
Invited session
Chair: Caston Sigauke, Statistics and Operations Research,
University of Limpopo, Bag X1106, Sovenga, 0727, Polokwane,
Limpopo, South Africa, csigauke@gmail.com
1 - Decision Support for Power Generator Maintenance
Scheduling
Jan van Vuuren, Logistics, University of Stellenbosch, Private
Bag X1, Matieland, 7602, Stellenbosch, Western Cape, South
Africa, vuuren@sun.ac.za, Bernard Schlunz
Maintenance of power generating units has to be coordinated carefully within
a power generating utility to ensure that power supply shortfalls do not occur
due to too many units being out of service simultaneously. This maintenance
scheduling problem is considered with scheduling objectives including
cost minimisation and maintaining power generation safety margins (due to
stochastic electricity demand), and with various practical constraints. A case
study is included for the South African power generating utility, Eskom.
2 - Using Metaheuristic Modelling to Further Freight Transport
Energy Management in South Africa
Tanya Visser, Industrial Engineering, Stellenbosch University,
Privaatsak X1, Matieland, 7602, Stellenbosch, South Africa,
tanyav@sun.ac.za
Transportation is a major sustainability engineering concern. The sector consumes
vast amounts of non-renewable energy, expediting resource depletion
and causing environmental harm. Improving sustainability within the sector is
a daunting, highly complex and multi-faceted task. Planning authorities require
decision support to enable decisions cognisant of all the intricacies surrounding
the interaction between transport and energy management. This paper showcases
how a purpose-built metaheuristic model aids in the formulation of freight
transport energy management strategies for South Africa.
3 - Modeling Daily Peak Electricity Load Forecasting in
South Africa using a Multivariate Non-parametric Regression
Approach
Caston Sigauke, Statistics and Operations Research, University
of Limpopo, Bag X1106, Sovenga, 0727, Polokwane, Limpopo,
South Africa, csigauke@gmail.com, Delson Chikobvu
Accurate prediction of daily peak load demand is very important for decision
makers in the energy sector. This helps in the determination of consistent and
reliable supply schedules during peak periods. Accurate short term load forecasts
enable effective load shifting between transmission substations, scheduling
of startup times of peak stations, load flow analysis and power system security
studies. A multivariate adaptive regression splines (MARS) modelling
approach towards daily peak electricity load forecasting in South Africa is presented
in this paper for the period 2000 to 2009. MARS is a non-parametric
multivariate regression method which is used in high-dimensional problems
with complex model structures, such as nonlinearities, interactions and missing
data, in a straight forward manner and produces results which may easily be
explained to management. The models developed in this paper consist of components
that represent calendar and meteorological data. The performances of
the models are evaluated by comparing them to a piecewise linear regression
model. The results from the study show that the MARS models achieve better
forecast accuracy.
IFORS 2011 - Melbourne FC-03
� FC-02
Friday, 15:15-16:45
Meeting Room 101
Scheduling
Stream: Scheduling
Contributed session
Chair: Murari Lal Mittal, Mechanical Engineering Department,
Malaviya National Institute of Technology Jaipur, JLN Marg,
302017, Jaipur, Rajasthan, India, mlmittal.mnit@gmail.com
1 - Aircraft Rotation Problem: Is there a Problem?
Torsten Reiners, University of Hamburg, Institute of Information
Systems, Von-Melle-Park 5, 20146, Hamburg, Germany,
torsten.reiners@gmail.com, Julia Pahl
We survey the literature of airline scheduling regarding not only the isolated aircraft
maintenance rotation problem, but also evaluate to what extend such (integrated)
planning and scheduling problems can be solved to optimality especially
regarding input data of real world problem sizes. In addition, we present
an overview of algorithms and (meta-)heuristics.
2 - Cost Optimization: Case Studies from the Construction
Industry
Miklos Hajdu, Department of Construction Management, Szent
Istvan University Ybl Miklos Faculty, Budapest Thököly út 74,
1146, Budapest, Hungary, hajdu.miklos@ybl.szie.hu
Despite the fact that the official history of project management started with a
cost optimization problem (Kelley & Walker 1958), these models have not become
widespread in project management practice. In this paper the algorithms
that operation research offers for project managers for project time-cost tradeoffs
will be summarized. In addition, some case studies from the construction
industry will be discussed, which — in our opinion — prove that cost optimization
ought to be an essential part of project management practice.
3 - A New Weight Varying Scheme for Particle Swarm Optimization
Algorithms
Murari Lal Mittal, Mechanical Engineering Department,
Malaviya National Institute of Technology Jaipur, JLN Marg,
302017, Jaipur, Rajasthan, India, mlmittal.mnit@gmail.com,
Abhinav Mittal
PSO is one of the recent metaheuristics successfully applied to a variety of OR
problems. Inertia weight is an important parameter in PSO algorithms varied
during iterations to effectively explore/exploit the search space. In most of
the existing weight varying schemes it is either increased or decreased during
successive iterations. In this paper we propose a scheme in which weight is
increased form minimum to the maximum in half of the iterations and then decreased
to the minimum in the rest half. The scheme is tested on instances of
the resource constrained multiproject scheduling problem.
� FC-03
Friday, 15:15-16:45
Meeting Room 102
AI and GameTheory
Stream: Contributed Talks
Contributed session
Chair: Janny Leung, Systems Engineering and Engineering
Management Dept, The Chinese University of Hong Kong, Shatin,
New Territories, Hong Kong, jleung@se.cuhk.edu.hk
1 - Feature Extraction and Classification in Pattern Recognition
and their Application in Economy
Pawel Blaszczyk, Institute of Mathematics, University of Silesia,
Bankowa 14 Street, 40-007, Katowice, Poland,
pblaszcz@math.us.edu.pl
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