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5 APL Lecture | 6 APL | 7 APL | 8 APL | 9 |

12 APL | 13 APL | 14 APL Lecture | 15 APL | 16 |

19 | 20 AGM | 21 | 22 | 23 Symposium |

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20 Lecture YP Forum | 21 Lecture | 22 | 23 | 24 |

27 Lecture | 28 | 29 | 30 | 31 Seminar |

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8 SISC 96 | 9 SISC 96 | 10 SISC 96 | 11 SISC 96 | 12 SISC 96 |

15 | 16 | 17 Workshop | 18 Workshop Optimization Day | 19 Workshop |

22 | 23 ISIS | 24 Lecture ISIS | 25 ISIS | 26 ISIS |

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19 | 20 ISIS | 21 ISIS | 22 ISIS | 23 ISIS |

26 Lecture | 27 | 28 Lecture | 29 ORSNZ | 30 ORSNZ |

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University of Melbourne

Thomas Cherry Room, Mathematics Department (Richard Berry Bldg)

Department of Mathematics

University of Iowa

Iowa City, IA 52242, USA

Abstract

Contact Danny Ralph, 344 5212, danny@mundoe.maths.mu.oz.au for more information.

Back to front.

RMIT, Building 8, Level 9, Room 66

The Pennsylvania State University

Victoria University of Technology, Footscray CampusRaceview Suite, Building P Ground Level

(Ample parking is available across Ballarat Road)

For more information, please contact:

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

Computer Science Department, The University of Melbourne, 221 Bouverie Street, Carlton

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

RMIT Building 8 Level 9 Room 66

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.

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

Prof. Robert H Storer

CSIRO Division of Mathematics & Statistics

Gate 7, 71 Normanby Rd Clayton Vic

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.

CONVEX ANALYSIS

Professor Do Van Luu

Institute of Mathematics, Hanoi, Vietnam

currently visiting University of Melbourne

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:

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

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

QUESTIONS:

Any enquiries should come to me, phone 9344 6761, or e'mail craven@mundoe.maths.mu.oz.au

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.

Two day Short Course

Monash Uni - Caulfield campus

More info' available from Paul Lochert

Dr A H Christer

Centre for Operational Research and Applied Statistics

University of Salford

RACEVIEW , VUT Footscray

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

Dr A H Christer

Centre for Operational Research and Applied Statistics

University of Salford

RACEVIEW , VUT Footscray

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

Chair: Dudley Foster

Speakers:Jeremy Howard - ACP, Vincent Yeo - Simsion Bowles Cathy McGurk - ASE

RMIT Building 8, Level 9, Room 66

Professor Guang-Ya Chen

Institute of Systems Science, Beijing, China

Richard Berry Building (Mathematics Dept.), University of Melbourne,Classroom C (in the First Year Learning Centre)

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.

Mathematics and Statistics Computing Laboratory

De La Salle University

Manila, Philippines

Theatre B, Richard Berry Building

Department of Mathematics

The University of Melbourne

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:

- their methodological foundations
- the prominent characteristics of their algorithms, and
- 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.

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.

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.

School of Information Systems

Swinburne University of Technology

Classroom C, Richard Berry Building

The University of Melbourne

Computer Sciences Department, University of Wisconsin -- Madison,1210 West Dayton St., Madison, Wisconsin 53706.

Thomas Cherry Room, Richard Berry Building, University of Melbourne

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'

OR and Systems Group

The Management School Imperial College

53 Prince's Gate London SW7 2PG

CSIRO, Division of Mathematics and Statistics Gate 7, No 71, Normamby Road, Clayton 3169

- 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.

- 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.

Department of Mathematics, The University of Melbourne

Thomas Cherry Room, Richard Berry Building, University of Melbourne

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.

School of Computing and Maths University of HuddersfieldUK

Thomas Cherry Room, Richard Berry Building, University of Melbourne

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.

Swinburne University of Technology

Room D704, Victoria University of Technology (Footscary Campus)

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

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

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?

Yours Optimizingly (optimistically ?),

Bruce Craven

Dr B D Craven, Math Dept, University of Melbourne

craven@mundoe.maths.mu.oz.au

16.5.96

Room M101, Victoria University of Technology (Footscary Campus)

- 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.

Tue 19 Nov

Jane Parkin

November 27, 1996

Executive 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

Edinburgh Road

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

- Peter Cerone
- Ex Offcio:
- Santosh Kumar

Department of Applied Mathematics

National University of Science & Technology

P O Box 346, Bulawayo

ZIMBABWE

E-Mail: MANGENA@esanet.zw

- Santosh Kumar

**OR Solutions**: Problem Solving through Quantitative Analysis

P.O.Box 2086

1/242 Bambra Road

Caulfield 3161

Tel: (03) 9578 6380

Fax: (03) 9578 2321

- Hearne Scientific Software
- SiliconGraphics

Mr Andrew Wyatt

357 Camberwell Rd

Camberwell VIC 3124

Tel: (+613) 9882 8211

Fax: (+613) 9882 8030

Crawl back to ASOR National

APORS'97