## 1996 Program

 January February March April May June July August September October November December

### January 1996

 Monday Tuesday Wednesday Thursday Friday 1 2 3 4 5 8 9 10 11 12 15 16 17 18 19 22 23 24 25 26 29MISG 30MISG 31MISG

### February 1996

 Monday Tuesday Wednesday Thursday Friday 1MISG 2MISG 5APLLecture 6APL 7APL 8APL 9 12APL 13APL 14APLLecture 15APL 16 19 20AGM 21 22 23Symposium 26 27 28 29Lecture

### March 1996

 Monday Tuesday Wednesday Thursday Friday 1 4Lecture 5 6 7 8 11 12 13 14 15 18 19 20AGM 21 22 25 26 27 28 29

### April 1996

 Monday Tuesday Wednesday Thursday Friday 1 2 3 4 5 8 9 10 11 12 15Lecture 16 17 18 19 22 23 24Lecture 25 26 29Seminar 30

### May 1996

 Monday Tuesday Wednesday Thursday Friday 1 2 3 6 7 8 9 10 13Lecture 1Lecture 2 14 15Lecture 16Lecture 17 20LectureYP Forum 21Lecture 22 23 24 27Lecture 28 29 30 31Seminar

### June1996

 Monday Tuesday Wednesday Thursday Friday 3Lecture 4 5 6 7 10 11 12Course 13Course 14 17 18 19Lecture 20 21 24 25 26 27 28

### July1996

 Monday Tuesday Wednesday Thursday Friday 1 2 3 4 5 8SISC 96 9SISC 96 10SISC 96 11SISC 96 12SISC 96 15 16 17Workshop 18WorkshopOptimization Day 19Workshop 22 23ISIS 24LectureISIS 25ISIS 26ISIS 29 30 31

### August1996

 Monday Tuesday Wednesday Thursday Friday 1 2 5 6 7Lecture 8 9 12 13 14 15 16 19 20ISIS 21ISIS 22ISIS 23ISIS 26Lecture 27 28Lecture 29ORSNZ 30ORSNZ

### September1996

 Monday Tuesday Wednesday Thursday Friday 2 3 4Lecture 5 6 9 10 11 12 13Lecture 16 17 18 19 20 23 24 25 26 27 30

### October1996

 Monday Tuesday Wednesday Thursday Friday 1 2 3 4 7 8 9 10 11 14 15 16 17 18 22 23Lect. and Conf. 24 25 26 29 30 31

### November1996

 Monday Tuesday Wednesday Thursday Friday 1 4 5 6 7 8 11 12Conference 13Workshop 14Workshop 15 18 19Workshop 20Workshop 21Workshop 22 25 26 27Workshop 28 29

### December1996

 Monday Tuesday Wednesday Thursday Friday 2 3 4 5 6 9 10 11 12 13 16 17 18 19 20 23 24 25 26 27 30

3:00PM Monday, February 5, 1996

University of Melbourne
Thomas Cherry Room, Mathematics Department (Richard Berry Bldg)

### An Infeasible--Interior--Point Algorithm for Semidefinite Programming

Florian A. Potra
Department of Mathematics
University of Iowa
Iowa City, IA 52242, USA

Abstract

Semidefinite programming (SDP) deals with finding the minimum or maximum of a linear function over a set of positive semi-definite matrices satisfying some linear equality and inequality constraints. Such optimization problems are very important in optimal control, but until very recently there were no efficient methods for their numerical solution. In the past couple of years different generalisations of the interior point methods developed for linear programming, have proved very effective in solving semidefinite programming problems arising in applications. We present a new primal-dual infeasible-interior-point path-following algorithm for solving general semidefinite programming problems. If the problem has a solution, then the algorithm is globally convergent. If the starting point is feasible or close to being feasible, the algorithms finds an optimal solution in at most $O(\sqrt{n}L)$ iterations. If the starting point is large enough then the algorithm terminates in at most $O(nL)$ steps either by finding a solution or by determining that the primal-dual problem has no solution of norm less than a given number. Moreover, we propose a sufficient condition for the superlinear convergence of the algorithm. In addition, we give two special cases of SDP for which the algorithm is quadratically convergent.

Back to front.

5:30pm, Wednesday February 14 1996

RMIT, Building 8, Level 9, Room 66

### DATA ENVELOPMENT ANALYSIS: AN OVERVIEW, SOME NEW IDEAS AND RATING HOSPITALS

Liam O'Neill, Holly Lewis and Keith Ord
The Pennsylvania State University

ABSTRACTS
We begin with an overview that describes the structure and aims ofDEA. We then present a new formulation, using notions of super-efficiency (SE), related to influential observations in regression.The SE approach eliminates the problem of multiple solutions, aswell as allowing data screening for outliers. The method isillustrated by application to previously published data on hospitalperformance.

2:00 pm - 3:00 pm Thursday February 29, 1996
Victoria University of Technology, Footscray CampusRaceview Suite, Building P Ground Level
(Ample parking is available across Ballarat Road)

### GENERATING INTEGER SOLUTIONS FOR THE ONE-DIMENSIONAL CUTTING STOCK PROBLEM

Speaker: Prof. Gerhard Waescher of Martin, Luther University of Halle (Germany) All are welcome.

Dr. Lutfar Khan (tel: 9688-4687 or khan@matilda.vut.edu.au) orProf. Bob Johnston (tel: 9905-3422 or rejohnston@eng.monash.edu.au)

5:30 pm, Wednesday, April 15, 1996
Computer Science Department, The University of Melbourne, 221 Bouverie Street, Carlton

## Expert Systems in Banking

David Burgos, CCN Asia Pacific

For details contact: Simon Goss 9626 7274, Ross Gayler 9643 8853, Julie Gledhill 9818 0795.

6:00 pm, Wednesday, April 24, 1996
RMIT Building 8 Level 9 Room 66

## Use of Distributed Interactive Simulation for Naval Training

Peter Ryan, Defence Science and Technology Organisation

Abstract

The Royal Australian Navy (RAN) plans to use Distributed Interactive Simulation (DIS) to enhance its team training capability. Initially it is planned to link trainers at the same location and later connect these to other external trainers and live assets both in port and at sea. Linking manned simulators helps to more closely imitate operational environments with different platforms (ships/aircraft) interacting and will provide more sophisticated and effective command team training for the RAN s surface warfare fleet. DIS will also allow these disparate simulators to be linked to the outside world for a wider range of tactical training. Integration of the trainers will also provide the ancillary benefits of course length reductions, system manning efficiencies, and increasing the training environment fidelity.

This paper discusses the application of DIS technology to the RAN s surface warfare operations room simulators. A general overview of DIS and other US modelling and simulation initiatives will also be provided.

3:30 PM, Monday 29 April 1996, CSIRO Division of Mathematics and Statistics, Gate 7, 71 Normamby Road, Clayton, VIC 3169
Location of Inspection Facilities on a Hazardous MaterialTransportation Network.
Pitu B. Mirchandani
Systems and Industrial Engineering Department
The University of Arizona
Tucson, AZ 85721
Abstract

Regulating agencies need to make choices on where to inspect trucks carrying hazardous goods. If the capacity of a facility to inspect is very large then locating the facilities to inspect as many trucks as possible becomes a m-cover problem. In the more practical case where the capacity is limited, we formulate a new location problem. We study its complexity and develop some heuristics and bounds for this problem. We will also introduce the concept of "inspection equilibrium" and providesome preliminary results on computing it.

Short Bio:

Pitu Mirchandani is a Professor and Department Head of Systems and Industrial Engineering at the University of Arizona. He has BS and MS degrees in Engineering from UCLA, and adoctoral degree in Operations Research from MIT. His researchinterests are in logistics, location, scheduling, and systems designwith applications in production, transportation, and manufacturing.He has published over 50 papers and two books on locationtheory.

Pitu Mirchandani is visiting CSIRO Division of Maths andStats for two days next week. He will be here on Monday29 April and Tuesday 30 April. We have organised a publicseminar for Monday 29 April (announcement follows). Allare welcome to attend.

If people would like to meet with Pitu separately, could they please ring me or email me their requests...

Thanks.

--Mohan

Mohan Krishnamoorthy, CSIRO, Div of Maths and Stats (OR Group),(Off) Bayview Avenue, Clayton 3168, Australia. Or: Pvt Bag 10, Rosebank MDC, Victoria 3169, Australia.(Res) 7, Wanda Street, Mulgrave, VIC 3170, Australia.Phone: (Off) +61 3 545 8042; (Res): +61 3 560 3305Fax: +61 3 545 8080; email: mohan@mel.dms.CSIRO.AUhttp://WWW.mel.dms.CSIRO.AU/~mohan/index.html

## Problem Space Search

### byProf. Robert H Storer

#### 3pm Monday, 13 May 1996 CSIRO Division of Mathematics & Statistics Gate 7, 71 Normanby Rd Clayton Vic

Abstract:

Problem Space Search is a new, simple, and effective approach forconstructing heuristics for combinatorial optimization problems. Problem Space Search utilizes quite different neighborhood structures than typical local search heuristics. These structures account primarily for its success. Problem Space Search has proven particularly effective for a wide variety of difficult scheduling problems. Problem space search applications to Bi-criteria machine scheduling, railway scheduling, and scheduling airplane landings will be introduced as time permits. The airplane landing research, begun less than a week ago, will illustrate the ease with which problem space search algorithms can be constructed.

Biograpy:

Dr. Robert H. Storer is Currently Associate Professor of Industrial andManufacturing Systems Engineering, and Co-Director of the Manufacturing Logistics Institute at Lehigh University, Bethlehem Pennsylvania, USA. He received a B.S. in Industrial and Operations Engineering for the University of Michigan, and M.S. and Ph.D. degrees in Industrial and Systems Engineering from Georgia Tech. His interests are in heuristics for combinatorial optimization problems, scheduling, and manufacturing applications.

## Lecture Series in CONVEX ANALYSIS

### By Professor Do Van Luu Institute of Mathematics, Hanoi, Vietnam currently visiting University of Melbourne

DATES: Mondays 13, 20, 27 May and 3 June
TIME : 3.15PM - 5.15PM
PLACE: Room G05, Richard Berry Building, University of Melbourne (on the ground floor of the Mathematics Department)

DESCRIPTION:

I have asked my research collaborator Prof. Do Van Luu (from Institute of Mathematics, Hanoi, Vietnam, visiting me for six months) to give some lectures for graduate students on a topic in Optimization. I have suggested Convex Analysis, since this important topic is only lightly treated in our regular courses. As well as graduate students, any other interested in Optimization are welcome to attend.

The proposed content is summarized as follows:-

SUMMARY:

1. Basic concepts Affine sets, convex sets and cones, convex functions,Caratheodory's theorem.
2. Topological properties Relative interiors ofconvex sets, recession cones and unboundedness, closedness.
3. Duality. Separation theorems, conjugates of convex functions, polars, relation between support function and indicator function.
4. Differential theory. Directional derivatives and subgradients, subdifferentials of convex functions.

The main reference is R. T. Rockafellar's book, Convex Analysis .

QUESTIONS:

Dr. B. D. Craven
1 May 1996

Parking is not available on campus without a permit. Perhaps yourinstitution can give you a University of Melbourne parking permit.

Wed June 12 and Thur June 13
Two day Short Course
Monash Uni - Caulfield campus

## A Stochastic Modelling Problem in a High-Tech Steel Production Plant

### byDr A H ChristerCentre for Operational Research and Applied StatisticsUniversity of Salford

#### 2-3pm Wed, 15 May 1996 RACEVIEW , VUT Footscray

Abstract:

The presentation reports on a maintenance modelling actionresearch study of the roll change equipment in a high volume steelmill. The equipment is of a "preparedness" variety, in that defectsare only recognisable when the equipment is required for use. Astochastic model of behaviour of the plant under various servicemaintenance systems is developed, and the consequences to productiontime of alternative service periods and service quality levelspredicted. The increased value in terms of insight given tomanagement of stochastic modelling over simpler options ishighlighted.

For further info - contact Peter Cerone - pc@matilda.vut.edu.au

## Modelling and Quality of Automatic Quality Checks

### byDr A H ChristerCentre for Operational Research and Applied StatisticsUniversity of Salford

#### 2-3pm Thur, 16 May 1996 RACEVIEW , VUT Footscray

Abstract:

High speed high volume automatic production techniques arecapable of producing defective as well as quality products. Toreduce the risk of the former, monitoring checks are commonlyintegrated wit hin a production process to both identify defectiveproduction and to monitor overall quality performance.

The paper addresses the problem of assessing the accuracy andutility of automatic quality tests used in high-speed production. Amethod is proposed for estimating the probabilities that the production process produces a product which is defective, that thenon-defective product will pass the test, and that a defectiveproduct fails the test. Given these estimates, it becomes possibleto deter mine the consequences to quality and output of using thetest in various ways.

Data for printed circuit manufacture are used to demonstrate themethod. Models of the effectiveness of various product testingprocedures are investigated, the expected net profit is calculatedand the probability of dispatching a defective product to a customerassessed.

For further info - contact Peter Cerone - pc@matilda.vut.edu.au

## On Generalized Ekeland's Variational Principle with Vector-Valued Functions

### by Professor Guang-Ya ChenInstitute of Systems Science, Beijing, China

#### Tuesday 21 May, 4.15 pm, May 1996 Richard Berry Building (Mathematics Dept.), University of Melbourne,Classroom C (in the First Year Learning Centre)

His topic is important in Optimization, and perhaps toDifferential Equations as well.

All interested are welcome to come. Direct any enquiries to meon 9344 6761 or craven@mundoe.maths.mu.oz.au . Sadly, we cannot offer you parking at Melbourne University, unless maybe you get a permit from another institution.

## On the Relationship between Branch-and-Bound and Dynamic Programming

### Dr Emmanual B. MacalalagMathematics and Statistics Computing Laboratory De La Salle University Manila, Philippines

#### Friday, 2:15 PM, 31 May 1996 Theatre B, Richard Berry Building Department of Mathematics The University of Melbourne

Abstract:
In this talk, we present an analysis of the relationship between two widely used optimisation approaches namely, branch-and-bound and dynamic programming. Our analysis focuses on three aspects:
1. their methodological foundations
2. the prominent characteristics of their algorithms, and
3. the factors that affect the efficiency of their algorithms.

We approach our analysis by separating the methodology from the computational aspects of the methods. This has led to a simple yet effective theoretical framework which combines branch-and-bound and dynamic programming together. This framework may also satisfy Marsten and Morin's call for a unifying framework for discrete optimisation.

## Solving Large Scale Production Planning Model

### David Sier, CSIRO Division of Mathematics and Statistics, and David Noble, Swinburne University of Technology

#### 6:30 PM, Wed, June 19, 1996 Building 8, level 9, Room 66, RMIT

Abstract

In this talk we discuss methods used to try and solve a large scaleMixed Integer Linear Programming Model of a production planning system for one of our clients.

At present, we can solve the problem using reduced data sets but atfull size, the input MPS input data file exceeds 200MB in size and the problem has many millions of variables.

This talk concerns work in progress and we consider different methods for reducing the problem size and the use of heuristics to deal with blending and product separation aspects of the problem.

## Problem Definition and Software Design for Rapid Prototyping of OR Solutions

### Dudley Foster

Abstract

Introducing Operational Research based systems to organisationswhich have never used OR before is very different from building an OR system for an organisation which is already "sold" on OR. For external consultants, there is a particular need to deliver benefitsquickly so that the client can sell the new ideas to others in thecompany. All of this puts pressure on the consultant to develop ways of working which meet these needs without compromising thetraditional OR virtues of rigorous problem definition and robustsystems. Workshops have proved a good way of speeding up theproblem definition process and a few simple, software principlesfacilitate the rapid error free development of (spreadsheet based)systems. All of the above will be illustrated with examples fromrecent con sultancy assignments with particular focus on a projectwhich involved the introduction of LP to an organisation which hadnever used it before.

## The NEOS Server for complementarity problems: PATH

### ProfessorMichael C. FerrisComputer Sciences Department, University of Wisconsin -- Madison,1210 West Dayton St., Madison, Wisconsin 53706.

#### 2:15 PM, Monday, August 26, 1996 Thomas Cherry Room, Richard Berry Building, University of Melbourne

Abstract:

We explain the notion of a mixed complementarity problem and how this includes as special cases the problems of square systems of nonlinear equations and the optimality conditions of nonlinear programs. The Network-Enabled Optimization System (NEOS) is the electronic clearing house of the Optimization Technology Center that allows optimization problems to be submitted and scheduled for solution on various local and remote machines using a variety of state of the art solvers. The process of submission of a complementarity problem to NEOS is briefly outlined.

We describe a particular solver, PATH, for the solution of mixed complementarity problems and give details of how this solver is connected to NEOS. These details include a description of how the Jacobian of the defining nonlinear function is derived using ADIFOR, and how the resulting problems are solved on available (idle) workstations at the University of Wisconsin using the CONDOR system.

This represents joint work with Jorge J. More'

## Two Talks!!!!!!

### Yazid SharaihaOR and Systems GroupThe Management School Imperial College53 Prince's Gate London SW7 2PG

#### 2pm-3.15pm, Wednesday 28 August 1996CSIRO, Division of Mathematics and Statistics Gate 7, No 71, Normamby Road, Clayton 3169

ABSTRACTS:

1. A Tabu Search Algorithm for the Capacitated Minimum Spanning Tree problem

The capacitated minimum spanning tree problem (CSP) is considered. Given a graph G=(V,A), where each vertex v(i) has a traffic load q(i), and each arc (i,j) has a cost c(i,j), the CSP is the problem of finding a minimum cost spanning tree connecting a given root vertex to all other vertices via a set of subtrees, such that each subtree is incident on the root by exactly one arc and has a capacity below a given threshold Q. We consider a tabu searchheuristic for the solution to the problem. The neighbourhood search is based on subtree "cut and paste" strategies. A data structure is proposed to facilitate the on-line updating of the subtrees. Computational results are reported on test problems.

2. A Graph-Theoretic Approach to Line Image Processing

In this paper, we propose a novel graph-theoretic approach to binary line image analysis. Examples of line images are signatures, Chinese characters and transportation networks. In particular, we consider the thinning or skeletonization problem to illustrate this approach. The image is first mapped onto a graph, where all subsequent operations are to be performed. Analogies between the topological structure of the image and the combinatorial structure of the graph are established. The skeleton-location problem is then decomposed into a series of optimization subproblems on the graph. The problem is essentially formulated as a discrete location problem on the graph. Computational results are presented to illustrate the practical application of this approach.

Yazid Sharaiha received his BSc(Eng) from Imperial College (1986), his MS(Eng) from University of California, Berkeley (1987) and his PhD in Operations Research from Imperial College (1991). He joined the academic faculty of Imperial College Management School in 1991 as a Lecturer in the Operations Research and Systems Group. His research interests include combinatorial optimization and graph theory and their application to image analysis, transportation problems, and newtork design.

## Computational Aspects of Mathematical Programs with Equilibrium Constraints

### Houyuan JiangDepartment of Mathematics, The University of Melbourne

#### 4:15PM, Wednesday September 4, 1996Thomas Cherry Room, Richard Berry Building, University of Melbourne

Abstract:
Mathematical programs with equilibrium constraints, and more generallymultilevel optimization problems, form a relatively new area in optimization and have found various applications in economic equilibria and engineering sciences. Basic theory such as first- and second-order optimality conditions has been extensively studied in previous work. Some numerical methods have been proposed recently. However, little if anything is known about the numerical performance of these methods. We implement some of these methods on a set of test problems with several key features that might affect their computational performance. Preliminary comparison can be made from our results.

## Vehicle Scheduling - Theory and Practice

### Jane ParkinSchool of Computing and Maths University of HuddersfieldUK

#### 3:30 PM, Friday September 13, 1996 Thomas Cherry Room, Richard Berry Building, University of Melbourne

Abstract:

This seminar provides a brief introduction to the textbook vehicle scheduling problem and discusses some of the most commonly used algorithms used to solve it. It then goes on to discuss the practical requirements of transport planners, giving some details of vehicle scheduling software currently available and case studies of companies summarising their achievements using vehicle scheduling packages. At the end of the seminar, a demonstration of a vehicle scheduling package will be provided for any interested attendees.

## Multi Criteria Decision Making; (An overview?)

### Fatemeh GhotbSwinburne University of Technology

#### 5:30 PM, Wed October 23, 1996 Room D704, Victoria University of Technology (Footscary Campus)

In this session I will give a quick overview of the MCDM methodologies and then will discuss the Analytic Hierarchy Process (AHP) and its extension on the hierarchies with dependent loops (networks with feedback, ANP). Some application areas will also be introduced.

## Conferences and Workshops

### ISIS: Information, Statistics and Induction in Science

Melbourne, Australia, 20-23 August 1996
Conference Chair: David Dowe
Co-chairs: Kevin Korb and Jonathan Oliver
Web site

Full information and registration forms available from Paul Lochert or (Dr.) David Dowe, Dept of Computer Science, Monash University, Clayton, Victoria 3168, Australia, dld@bruce.cs.monash.edu.au Fax:+61 3 9905-5146

### SISC-96

July 8-12 1996, Sydney International Statistical Congress, Sydney

If you require further information and Registration forms contactConference Action Pty Ltd, PO Box 1231, North Sydney, NSW 2059

### Second Australia-Japan Workshop on Stochastic Models

July 17-19, 1996, ANA Hotel, Gold Coast Queensland

## Optimization Day 1996

### July 18 1996

Dear Optimizing colleague,
A First Cirular was sent out, earlier this year, aboutthe planned Optimization Day at Melbourne University (Math. Dept.)on July 18. This continues the series of Optimization Days, heldin previous years at Univ. of Ballarat and Univ. of NSW.

I have received e'mail from three people who would like to attend, two at least of the three offering papers. However, weneed some more people to present papers, and to attend and listen, inorder to have a viable one-day conference. I am going overseas in earlyJune (will be back for July 18) - so conference organization must bedone before I depart.

Please tell me *quickly*, by e'mail :

• Would you like to give a paper at this Optimization Day? If yes, then tell me an approximate title, so I can advertise it.
• Would you like to attend the Optimization Day, without being committed to presenting a paper?
Talks on various aspects of Optimization, including puretheory, applications, computational aspects, or mixtures of these,will be welcome, and of interest.

Yours Optimizingly (optimistically ?),
Bruce Craven
Dr B D Craven, Math Dept, University of Melbourne
craven@mundoe.maths.mu.oz.au
16.5.96

## Student Conference

#### 1 - 5 PM, Wed October 23, 1996 Room M101, Victoria University of Technology (Footscary Campus)

Final Call for Expressions of Interest. Students discuss presenting your work with your supervisor. Academics spread the word and encourage student participation. A balance of applied project reports (UG as well as PG) and research results is sought.

RECENT ADVANCES Tuesday November 12 1996 RMIT PROGRAMME 8:45 - 9:10 Registration9:10 Opening9:15 - 10:45Solving Piecewise Affine Optimization Problems - Approaches and Applications P. Neame,N. Boland and D. RalphThe Personnel Task Scheduling Problem: Formulations and AlgorithmsMohan Krishnamoorthy, Andreas Ernst and John BeasleyConstructing a General Solving Environment for Operations Research:The Case of the GSF ProjectEmmanuel Macalalag and Peter Davison 10:45-11:15 Morning tea11:15 -12:15Hierarchical Modelling of Optimal Regulation of Municipal Water PollutersJ.B. Krawczyk A heuristic for solving a Maintenance Scheduling Problem Dr Ewa Swierczak 12:15-1:15 Lunch1:15 - 2:45The Role of Branch and Bound and Dynamic Programming in Algebraic ModellingLanguages for Combinatorial OptimizationEmmanuel Macalalag and Moshe Sniedovich OPTIMAL CONTROL FOR AN OBSTRUCTION PROBLEM B D Craven, University of MelbourneMinuteMan - an Interior Point System for Solving Linear Programming Problems. Peter Davison, Alexander Tsvelikh, John Orr, and Gavin Dober2:45-3:05 Afternoon Tea3:05-4:05 A Shortest-Path Based Algorithm for Multiple-Allocation p-Hub Median ProblemsAndreas T. Ernst and Mohan KrishnamoorthyOptimal Carryover Storage Level with Markovian InputsPaul Lochert4:05 - Discussion and refreshments

• Wed 13 Nov: Risk analysis, Scenario analysis
• Thur 14 Nov: Constrained optimisation, Linear programming
• Wed 20 Nov: Statistical modelling, regression analysis
• Thur 21 Nov: Forecasting

* Excel is a registered trade mark of Microsoft.

one-day workshop
Tue 19 Nov
Jane Parkin

## Practical Simulated Annealing

One Day Workshop
November 27, 1996

### ASOR Melbourne ChapterExecutive Committee 1996/7

• Chair: Steve Weal
Department of Mathematics
Swinburne University of Technology
P.O.Box 218
HAWTHORNE VIC 3121
(W)9728 7131
(F) 9819 0821
(E) sew@stan.xx.swin.oz.au

• Vice-Chair: Kaye Marion
Department of Statistics and Operations Research
RMIT
360 Swanston Street
Melbourne 3000
(W) 9660 3162
(F) 9660 2454
(E)k.marion@rmit.edu.au

• Secretary: Baikunth Nath
Gippsland School of Computing and Information Technology
Monash University
Churchill, Vic 3842
(W) 9902 6468
(F) 9902 6842
(E) b.nath@@fcit.monash.edu.au

• Treasurer: Lutfar Khan
Victoria University of Technology
P.O.Box 14428 MMC
Melbourne 3000
(W) 9790 4687
(F)
(E) Khan@matilda.vut.edu

• Committee:
• Peter Cerone
Department of COmputer Mathematical Sciences
Victoria University of Technology
P.O.Box 14428 MMC
MELBOURNE 3000
(W) 9688 4689
(F) 9687 7632
(E) pc@matilda.vut.edu.au

• Paul Lochert
Department of Mathematics and Sciences
Monash University - Caulfield Campus
P.O.Box 197
CAULFIELD EAST 3145
(W) 9903 2337
(F) 9903 2227
(E) p.lochert@maths.monash.edu.au

• Cathy McGurk
A.S.E.
(W) 9882 7522

• David Noble
Department of Mathematics
Swinburne University of Technology
LILYDALE 3140
(W) 9 728 7134
(F) 9728 7139
(E) dnoble@banyan.swin.edu.au

• Moshe Snieodvich
Department of Mathematics
University of Melbourne
PARKVILLE 3052
(W) 9344 5559
(F) 9344 4599
(E) moshe@maths.mu.oz.au
(WWW) http://www.maths.mu.oz.au/~moshe/

• Patrick Tobin
Department of Mathematics
Swinburne University of Technology
P.O.Box 218
HAWTHORNE 3121
(W) 9819 8013
(F) 9818 3645
(E) pct@stan.xx.swin.oz.au

• Poli Konstantinidis (Student Member)
Department of Mathematics
RMIT
Melbourne 3000

• Ex Offcio:
• Santosh Kumar
Department of Applied Mathematics
National University of Science & Technology
P O Box 346, Bulawayo
ZIMBABWE
E-Mail: MANGENA@esanet.zw

### Consulting Services

• OR Solutions: Problem Solving through Quantitative Analysis
P.O.Box 2086