Melbourne Chapter


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2003 Program

Date Day Time Where Event Speaker Title
March 19 Wed 5:30PM RMIT 8:9:66 AGM
March 19 Wed 6PM RMIT 8:9:66 Lecture Mike Wright Reflections on twelve years of cricket scheduling and its spin-offs
April 16 Wed 6:30PM RMIT 8:9:66 Lecture Mike Pidd A TAXING PROBLEM: HARD AND SOFT OR IN PUBLIC POLICY
May 21 Wed 6:00PM RMIT 8:9:66 Lecture Ury Passy Data envelope analysis modelling
June 18 Wed 6:00PM RMIT 8:9:66 Lecture Marimuthu Palaniswami Machine Learning for Real-Time Applications
August 13 Wed 6:00PM RMIT 8:9:66 Lecture John Hearne Optimising Revenue from Game Ranches
September 17 Wed 6:00PM RMIT 8:9:66 Lecture Russell Thompson Estimating the benefits of considering travel time variability in the vehicle routing problem with time-windows
October 22 Wed 6:00PM RMIT 8:9:66 Lecture Andrew Wirth Scheduling: a suitable case for treatment
November 12 Wed 6:00PM RMIT 8:9:66 Mini Conference ???? Recent Advances in OR

AGM
WHEN5:30-6:00M, Wednesday, March 19, 2003
WHERERoom 8:9:66, RMIT
ABSTRACT

AGM - followed by a lecture (see below)

CONTACTPaul Lockert, E-mail: paul.lochert@sci.monash.edu.au

Lecture
TITLE Reflections on twelve years of cricket scheduling and its spin-offs
SPEAKERMike Wright, Management Science Department at Lancaster University, UK
WHEN6:00-7:00M, Wednesday, March 19, 2003
WHERERoom 8:9:66, RMIT
ABSTRACT

This talk describes some cricket scheduling problems which the author has been addressing in practice over several years. These involve timetabling county fixtures in England and the allocation of umpires to these and other matches. This work has spawned several novel approaches which will be briefly described.

The problems are formulated as combinatorial objective problems with many objectives and constraints. The solution approach is to use metaheuristic search techniques. Issues covered include problem definition, the incorporation of multiple objectives, neighbourhoods, metaheuristic search algorithms and general lessons learned. .

CONTACTPaul Lockert, E-mail: paul.lochert@sci.monash.edu.au


Lecture
TITLE A TAXING PROBLEM: HARD AND SOFT OR IN PUBLIC POLICY
SPEAKERMike Pidd, Management Science Department at Lancaster University, UK
WHEN6:30-7:30M, Wednesday, April 16, 2003
WHERERoom 8:9:66, RMIT
ABSTRACT

Soft OR is now recognised as a valid approach esepcially when tackling ill-structured problems. That soft OR may be combined in practice with more conventional hard approaches is not in doubt, yet there are few case studies illustrating how this may be done. Recently, the UK Inland Revenue undertook a strategic review of the UK's personal tax system. A team composed of Revenue staff, Lancaster University staff and external consultants undertook this review using a combination of hard and soft approaches. The seminar will desrcibe the work done and will highlight the complementarity between the hard and soft approaches.

Mike Pidd is Professor of Management Science in the Management School at Lancaster University in the UK. He has served as Head of the Department of Management Science (formerly known as the OR Department) and is on sabbatical leave from his current role as Research Dean in the 5* rated Management School.

In 2000/2001 he was President of the Operational Research Society and was a member of the Business and Management Panel for the UK's 2001 Research Assessment Exercise. His research interests include computer simulation methods, procedural decision support and OR in public policy. He is known for two books: 'Computer Simulation in Management Science' (now in its 4th Ed) and 'Tools for Thinking: modelling in management science' (2nd Ed just published).

He believes that the most interesting things in OR/MS and in other management subjects take place on the boundary between theory and practice.

CONTACTPaul Lockert, E-mail: paul.lochert@sci.monash.edu.au
Lecture
TITLE Data envelope analysis modelling
SPEAKERUry Passy, Technion - Israel institute of Technology, Israel
WHEN6:00-7:00M, Wednesday, May 21, 2003
WHERERoom 8:9:66, RMIT
ABSTRACT Data envelope analysis (DEA) is a linear programming based technique for measuring the relative performance of organisational units where the presence of multiple inputs and outputs makes comparisons difficult.

DEA can be found in an impressive list of areas such as educational departments (schools, colledges, and universities), health care (hospital, clinics), prisons, agricultural production, banking, armed forces, sports, market research, transportation (highway maintanenance), courts, benchmarking, index number construction, and so on. In these examples, it is used as a generalised ranking procedure. However, consideration of various models in the dual space also provides insights into the Theory of Production.

This talk is an introductory presentation of DEA modelling, starting with the work of Charnes and Copper in Linear Fractional Programming, DEA as a generalised ranking procedure, a discussion of various models in DEA, the dual and its relation to the Theory of Production and of the various models in the dual space.

About Prof Ury Passy:

Prof Passy's research is primarily in the area of nonlinear optimisation. His main contributions have been in geometric programming, generalised convexity, and vector maximisation. In recent years, Prof Passy's work has dealt with decomposition of nonlinear programming for parallel computations, large allocation problems in distribution centres, and production theory - efficiency.

CONTACTPaul Lockert, E-mail: paul.lochert@sci.monash.edu.au
Lecture
TITLE Machine Learning for Real-Time Applications
SPEAKERMarimuthu Palaniswami,, University of Melbourne
WHEN6:00-7:00M, Wednesday, June 18, 2003
WHERERoom 8:9:66, RMIT
ABSTRACT

In recent years, Machine Learning has become a focal point in Artificial Intelligence. Support Vector Machines (SVMs) are a relatively new, general formulation for learning machines. SVMs perform exceptionally well on pattern classification, function approximation, and regression problems.

This talk is aimed at introducing researchers to Support Vector Machines; to motivate and explain how SVMs are used; and to demonstrate their potential both through real world and simple examples. Quadratic programming is used as a central element in the solution to learning problem from large high dimensional data. A practical signal processing example that will be used in the talk comes from fisheries management: how can remote video sensing be used in the real-time identification of fish species in rivers? The problem brings out significant issues such as learning, re-learning, and global convergence and the results presented will highlight some of the recent solutions. The examples presented demonstrate a need for analysis of large amounts of data to interpret or predict properties of a very complex environment.

Marimuthu Palaniswami obtained his B.E. (Hons) from the University of Madras, M.Eng. Sc. from the University of Melbourne, and Ph.D from the University of Newcastle, Australia. He is an Associate Professor at the University of Melbourne, Australia and also serves as Deputy Director for Centre for Networked Decision and Sensor Systems.

His research interests are in the fields of machine learning, nonlinear dynamics, computer vision, and intelligent control. He has published more than 180 conference and journal papers in these topics. He was an Associate Editor of the IEEE Tran. on Neural Networks and is on the editorial board of a few computing and electrical engineering journals. He served as a Technical Program Co-chair for the IEEE International Conference on Neural Networks, 1995 and was on the programme committees of a number of international conferences. He has given several invited tutorials and keynote talks in major international conferences and workshop meetings. Several successful industry sponsored projects come from National Australia Bank, Broken Hill Propriety Limited, Defence Science an Technology Organisation, Integrated Control Systems Pty Ltd, and ANZ Bank. He also received several ARCs, APA(I)s, ATERBS, DITARD and DIST grants for both fundamental and applied projects. He is also a recipient of foreign specialist award from the Ministry of Education, Japan.

CONTACTPaul Lockert, E-mail: paul.lochert@sci.monash.edu.au

Lecture
TITLE Optimising Revenue from Game Ranches
SPEAKERJohn Hearne, RMIT
WHEN6:00-7:00M, Wednesday, August 13, 2003
WHERERoom 8:9:66, RMIT
ABSTRACT

Private game ranching is a rapidly growing enterprise in South Africa. Many former cattle farms in semi-arid areas have changed to game. This is good for conservation but the profit motive is at the heart of these changes. The industry is relatively new and the development of strategies that increase profits could further tilt the balance towards game in marginal cattle farming areas and in this way increase the survival chances of African wildlife. The problem of determining an optimal mix of species will be discussed. This can be formulated as a linear programming problem. An illustration of the column generation method to solve this problem will be presented. Having determined optimal population numbers for each species the problem of managing capital and other resources during the conversion process from cattle will be formulated. This multiperiod problem yields an optimal sequence of acquisitions and sales that minimize the extremely large sums of capital required to set up a game ranch. Some results will be presented. Some further formulations of game management problems will be presented with results. Similarities between game management and optimal portfolio management will be mentioned.

CONTACTPaul Lockert, E-mail: paul.lochert@sci.monash.edu.au

Lecture
TITLE Estimating the benefits of considering travel time variability in the vehicle routing problem with time-windows
SPEAKERRussell G. Thompson, Department of Civil and Environmental Engineering, University of Melbourne
WHEN6:00-7:00M, Wednesday, September 17, 2003
WHERERoom 8:9:66, RMIT
ABSTRACT

Recent developments in vehicle monitoring technology such as global position systems allow traffic network performance data to be collected easily and cheaply. However, new procedures are required to utilise the increased range of data that is available. This seminar will present relationships that allow the variable nature of travel times to be incorporated within optimisation procedures for the vehicle routing problem with time windows. Closed form expressions were derived for determining the expected penalty cost associated with truck arrivals at customers with time windows when the travel time between customers is normally distributed. Stochastic programming techniques were used to incorporate the variability of travel times within the vehicle routing problem. The benefits of explicitly considering the random nature of travel times between customers were estimated using a case study based on a distribution problem in Melbourne.

CONTACTPaul Lockert, E-mail: paul.lochert@sci.monash.edu.au

Lecture
TITLE Scheduling: a suitable case for treatment
SPEAKERAndrew Wirth, University of Melbourne
WHEN6:00-7:00M, Wednesday, October 22, 2003
WHERERoom 8:9:66, RMIT
ABSTRACT

Abstract: Scheduling research goes back at least fifty years. Yet it has been suggested that insufficiently many practical gains have been achieved. In this seminar I hope to discuss some of the current research trends as well as the apparent gap between theory and practice. Examples drawn from the department¼s work, such as scheduling with setups and shared servers as well as robust and on-line approaches, will be used to illustrate these issues.

CONTACTPaul Lockert, E-mail: paul.lochert@sci.monash.edu.au

Mini Conference
TITLE Recent Advances in OR
WHEN9:15AM - 3PM, Wednesday, November 12, 2003
WHERERoom 8:9:66, RMIT

Programme
 

9:15 - 9:30 REGISTRATION


9:30

Discrete Ordered Median Problems: Dealing with Generalized Objective Functions in Facility Location

Natashia Boland, University of Melbourne, reporting on joint work with: Patricia Dominguez-Marin, Stefan Nickel and Justin Puerto

Abstract:
Facility location problems are of critical importance in logistics. However there are a wide range of criteria commonly used to assess the quality of a solution. For example, the total distance of all customers from their nearest facility, or the maximum distance of any customer from its nearest facility, are both used. Combinations of these, and more complex functions, are often of interest in logistics applications. Recently, a facility location problem with a generalized objective function has been proposed, that captures each of these criteria as a special case. Known as the Discrete Ordered Median Problem, it is, however, very difficult to solve. In this talk the problem will be described, integer programming models for it discussed, and a specialized branch-and-bound method presented.


10:00

MODEL FOR A SPECULATIVE BUBBLE

B. D. Craven, University of Melbourne

Abstract
A mathematical model is proposed for a speculative stock market, relating to a boom and bust period, during which psychological factors (optimism) count for more than economic fundamentals. A MATLAB simulation shows that the model captures many of the qualitative features observed during such periods. (But it does not predict the date of the crash.)


10:30

Scheduling Jobs with Forbidden Zones

Amir Abdekhodaee and Andreas Ernst, CSIRO Mathematical and Information Sciences , Clayton, Vic 3169, Australia

Abstract
The problem of sequencing ships for a berth was considered where tidal constraints restrict the movement of ships in and out of the berth. However, there is no restriction on berth loading/unloading operations even when there is a low tide. The objective is to sequence the ships, with not necessarily identical processing times, so that the completion time of the last ship's operation can be minimised. We show that the problem is NP-hard and provide some numerical results based on an integer programming approach to the problem.


11:00-11:30 Morning Tea


11:30

Writing your own DP computer codes: a practitioner oriented guide

Moshe Sniedovich, Department of Mathematics and Statistics, The University of Melbourne, Parkville, VIC 3052

Abstract:
Unlike the case of linear programming, there is no such thing as a "general purpose dynamic programming computer code". Consequently, in practice practitioners and researchersİ may have to write their own dynamic programming codes to suit their particular needs. There are clear indications that this is not always a straightforward task. In this presentation we shall discuss some of the fundamental practical aspects of developing dynamic programming codes for practical applications as well as research projects. The emphasis will be on key issues rather than details pertaining to specific computer platforms, problem instances and user interfaces. The discussion as a whole has been motivated by the speaker's experience over the last 35 years and is based on consulting and research activities in this area.


12:00

Rendezvous-evasion search in two boxes with incomplete information

S. Gal (University of Haifa) and J. V. Howard (London School of Economics)

Abstract:
An agent (who may or may not want to be found) is located in one of two boxes. At time 0 suppose that he is in box B. With probability p he wishes to be found, in which case he has been asked to stay in box B. With probability 1-p he tries to evade the searcher, in which case he may move between boxes A and B. The searcher looks into one of the boxes at times 1, 2, 3, .... . Between each search the agent may change boxes if he wants. The searcher is trying to minimise the expected time to discovery. The agent is trying to minimise this time if he wants to be found, but trying to maximise it otherwise. This paper finds a solution to this game (in a sense defined in the paper), associated strategies for the searcher and each type of agent, and a continuous value function v(p) giving the expected time for the agent to be discovered. The solution method (which is to solve an associated zero-sum game) would apply generally to this type of game of incomplete information.


12:30 - 1:30 Lunch


1:30

Predicting the Outcome of the 2003 Rugby World Cup

Stefan Yelas and Stephen Clarke, Swinburne University of Technology

Abstract:
A simple forecasting model was built in Excel to predict the results of each game and the tournament as a whole in the 2003 Rugby World Cup. An exponential smoothing technique was used to update the team ratings after each game, and optimised on all 566 games between the 20 World Cup teams from 1996. The model predicted the winning team, the margin of victory and the probability of a win. A tournament simulator used these predicted probabilities to calculate a team's chance of placing at any given time during the tournament. Match and tournament predictions have been regularly updated on our web site www.swin.edu.au/sport. The model has selected the correct winner of all 40 pool games, and the predicted margins have been used for profitable gambling.


2:00

Once upon a time there was exponential smoothing.

Ralph D Snyder, Department of Econometrics and Business Statistics, Monash University, Australia.

Abstract:
A revised version of the exponential smoothing method of forecasting is described. It is distinguished from its earlier incarnation by its reliance on sound statistical principles for maximum likelihood estimation, prediction and model selection. This new framework is contrasted with other methods of forecasting such as the Box Jenkins and state space approaches. It is argued that exponential smoothing is the most practical time series approach for tackling forecasting problems encountered in business, economics and finance.


2:30 Afternoon tea


Close

CONTACTPaul Lockert, E-mail: paul.lochert@sci.monash.edu.au


EXECUTIVE COMMITTEE 2002/03

tr>
Chairperson: Kaye E. Marion (Ms)
Department of Statistics & OR
RMIT
360 Swanston Street
MELBOURNE 3000
E-mail: K.Marion@rmit.edu.au
Phone: (w) +613 9925 3162
Fax: +613 9925 2454
Vice Chairperson: Natashia Boland (Dr)
Department of Mathematics and Statistics
The University of Melbourne
Parkville VIC 3052
E-mail: n.boland@ms.unimelb.edu.au
Phone: (w) +613 8344 5547
Fax: +613 8344 4599
Secretary: Patrick Tobin (Mr)
School of Mathematical Sciences
Swinburne University of Technology
P O Box 218
HAWTHORN 3121
E-mail: ptobin@swin.edu.au
Phone: (w) +613 9214 8013
Fax: +613 9819 0821
Treasurer: Paul Lochert (Assoc Prof)
Department of Mathematics
Monash University
P.O. Box 197
CAULFIELD EAST 3145
E-mail: P.Lochert@sci.monash.edu.au
Phone: (w) +613 9903 2647
Fax: +613 9903 2227
Committee: Lutfar Khan (Dr)
Department of Computer and Mathematical Sciences
Victoria University of Technology
P O Box 14428, MCMC
MELBOURNE 8001
E-mail: Khan@matilda.vut.edu.au
Phone: (w) +613 9688 4687
Fax: +613 9688 4050
Dudley Foster (Mr)
23 Wolseley Crescent
BLACKBURN 3130
E-mail: dudley@ozemail.com.au
Phone: (w) +613 9894 0355
Fax: +613 9894 0244
Mobile: 0417 342 272
Santosh Kumar (Dr)
Department of Mathematics and Statistics
The University of Melbourne
Parkville VIC 3052
E-mail: s.kumar@ms.unimelb.edu.au
Leonid Churilov (Dr)
School of Business Systems
Monash University
CLAYTON 3168
E-mail: Leonid.Churilov@fcit.monash.edu.au
Phone: (w) +613 9905 5802
Fax: +613 9905 5159
Harry Gielewski
E-mail: harrygie@ozemail.com.au
Mohan Krishnamoorthy E-mail: Mohan.Krishnamoorthy@csiro.au
Student Representative: Amando Rodado
Email: a.rodado@ms.unimelb.edu.au
Editor: Harry Gielewski
E-mail: harrygie@ozemail.com.au
Office Manager: Kaye E. Marion (Ms)
Department of Statistics & OR
RMIT
360 Swanston Street
MELBOURNE 3000
E-mail: K.Marion@rmit.edu.au
Phone: (w) +613 9925 3162
Fax: +613 9925 2454
Ex-Officio:: Moshe Sniedovich (Dr)
Department of Mathematics and Statistics
University of Melbourne
Parkville 3052
E-mail: m.sniedovich@ms.unimelb.edu.au
Phone: (w) +613 9344 5559
Fax: +613 9344 4599


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