ASOR Seminar Schedule
Updated 28 May 2021
ASOR's Thursday Afternoon Seminars are back! Starting 17 June 2021 we will be running these fortnightly at 4.30pm AEST (4pm SA, 2.30pm WA).
These seminars are via Zoom. They are open to members and non-members alike, and feature a 20-25 minute presentation plus Q&A afterwards. The Zoom meeting details are sent by email to our mailing list subscribers.
We will be recording these sessions with the presenters' permission and posting them on our vimeo account with links from this page (note that it can take us several weeks to edit and approve the recordings, and even longer sometimes, sorry!).
17 June 2021
Reena Kapoor and Rodolfo García-Flores (CSIRO Data61)
Optimal Schedules for Corn Planting and Storage
Corn (or maize) is, with rice and wheat, one of the most consumed cereals in the world, together accounting for 94% of all cereal consumption. It is estimated that, in 2012, the total world production of corn was 875.23 million tonnes. The development of seeds with desirable traits typically requires many years of in-field testing before new products can be delivered to market. Recently, innovative genomic technologies have shortened the time required to develop new corn hybrids, that is, new products that can deliver higher-yielding, better-adapted seed options for growers at a faster pace. However, higher yields and increased rates of produced parental lines introduce many new challenges. In this presentation, we address one such challenge, namely, the problem of managing the demands on storage facilities to cope with increasing output (i.e., the number of harvested ears). The problem was proposed by Syngenta Seeds to improve their year-round breeding process by optimizing planting schedules to achieve a consistent output, which translates into a weekly harvest quantity. Erratic weekly harvest quantities create logistical and productivity issues. The research question we address is: How can we optimally schedule the planting of our seeds to ensure that when ears are harvested, facilities are not over capacity, and that there is a consistent number of ears each week? The solution we present is the winner of the 2021 Syngenta Crop Challenge in Analytics.
1 July 2021
Dr. Ismail Ali (UNSW ADFA)
A novel differential evolution mapping technique for generic combinatorial optimization problems
15 July 2021
Dr. Ripon Chakrabortty (UNSW/ADFA)
Merging Data Analytics and Decision Analytics towards Project Management Roadmap: Future Perspectives
29 July 2021
Validation to Manage Model Risk
5 November 2020
Lessons learnt from COVID-19 surge modelling for the Australian Royal Flying Doctor Service
Hannah Johns (Florey Institute / U. Melbourne)
12 November 2020
Resources and methods for fire risk analysis
Simon Dunstall (CSIRO Data61)
19 November 2020
Gurobi v9.1 (releasing this week!) capabilities, new features and performance
Sebastian Thomas, Account Director – Oceania and Southeast Asia, Gurobi Optimization, LLC
and Kostja Siefen, Technical Account Manager, Gurobi
3 December 2020
A matheuristic solution approach for the p-hub center and routing problem over incomplete hub networks
Zühal Kartal, Eskisehir Technical University, Turkey
10 December 2020
A Mathematical Modelling Approach for Managing Sudden Risk in Supply Chain
Sanjoy Paul, University of Technology Sydney, and 2018 ASOR Rising Star Award Recipient
25 June 2020
Predicting solutions of large-scale optimization problems via machine learning: A case study in blood supply chain management
Zahra Hosseinifard (Lecturer of Operations Management, Faculty of Business and Economics at The University of Melbourne)
- Zahra will be talking us through her recently-published co-authored paper in Computers & Operations Research.
2 July 2020
Simulating the spread of COVID-19
-- Recorded presentation is hosted on vimeo at https://vimeo.com/437015924
Phil Kilby (Principal Research Scientist, CSIRO Data61)
- Phil is part of a team based in CSIRO and Department of Health which is looking at the dynamics of COVID-19 outbreaks and our response to keeping these outbreaks under control.
9 July 2020
Big Data Analytics and Machine Learning for Smart Cities
-- Recorded presentation is hosted on vimeo at https://vimeo.com/437779815
Peter Ryan (Honorary Research Fellow, Defence Science & Technology Group) and Richard Watson (Research Scientist)
- Peter, Richard and colleagues have been looking at the application of analytics to cities, with an emphasis on open data sets provided by City of Melbourne.
16 July 2020
An Application of Business Rule Optimisation
-- Recorded presentation is hosted on vimeo at https://vimeo.com/442309070
Alan Dormer (Opturion P/L and Monash University)
- Alan's recently awarded PhD is on the optimal choice of business rules for recurring decisions in domains such as customer service, finance and health.
23 July 2020
Performance analysis and feasibility of hybrid ground source heat pump systems in fourteen cities
-- Recorded presentation is hosted on vimeo at https://vimeo.com/517666880
Hansani Weeratunge (U. Melbourne)
- Hansani has recently completed a PhD applying simulation and optimisation to the design and operation of energy-efficient building heating and cooling systems based on ground-source heat pumps.
30 July 2020
Hyper-heuristic for Combinatorial Optimisation Problems
-- Recorded presentation is hosted on vimeo at https://vimeo.com/517667192
Ayad Turky (Victoria University)
- Hyper-heuristic (HH) is a high-level search methodology that searches for problem solving methods, rather than problem solutions, and can be succesfully applied in resource allocation, scheduling, routing, production planning and economic systems.
6 August 2020
Multiperiod storage system modelling in the context of nonlinear power network optimisation
-- Recorded presentation is hosted on vimeo at https://vimeo.com/517667505
Frederik Geth (Research Scientist, CSIRO Energy)
- Combining multiperiod storage models and power flow physics results in large nonlinear optimisation problems. The talk discusses the application of recent reformulation techniques and implementation aspects in Julia/JuMP/PowerModels.
13 August 2020
A decomposition framework for capacity expansion planning with unit commitment
-- Recorded presentation is hosted on vimeo at https://vimeo.com/517667756
Semini Wijekoon (Monash University)
- Development of an approach for solving electricity network design problems that is derived from scenario decomposition (SD) techniques.
20 August 2020
An OR fireside chat
27 August 2020
The use of the Sports Synthesis model to determine appropriate draft penalties in an AFL-like sports league
-- Recorded presentation is hosted on vimeo at https://vimeo.com/517668131
Geoff Tuck (CSIRO Oceans and Atmosphere, Hobart)
- Geoff will describe how a simulation model can be used to quantify the impact on success of alternative draft penalties for a club that has breached league regulations – and how this relates to fishing.
3 September 2020
Simulation-based optimization in fleet management
Hasan Turan (UNSW/ADFA)
10 September 2020
Online Incentive-Compatible Mechanisms for Traffic Intersection Auctions
David Rey (UNSW, Research Centre for Integrated Transport Innovation)
23-25 September 2020
Asia-Pacific Operations Research Societies (APORS) Virtual Conference
Online from international venues - see IFORS website for registration
Sci+Tech in the City - suspended due COVID-19
Sci+Tech in the City is held Fortnightly on Thursdays 5pm to 6.30pm in the Data61 Demonstration Lab, 710 Collins St, Docklands, Melbourne. Sci+Tech in the City is co-presented by ASOR, CSIRO Data61, RiskLab Australia and CSIRO Alumni.
Details of Past ASOR Seminars
2 June 2017 - Nicholas Davey
The ASOR Melbourne AGM on 2 June 2017 was preceded by a seminar Optimal road design through ecologically sensitive areas considering animal migration dynamics delivered by Nicholas Davey (University of Melbourne).
30 September 2015 - Andreas Ernst
A retrospective of over two decades of Operations Research at CSIRO
Abstract: The Operations Research group at CSIRO was formed in the early 1990s and since then has been a significant part of the OR scene in Australia. With the departure of a number of key members of the group and the imminent merger between NICTA and the CSIRO, the OR group will cease to exist in its current form. This talk will look back over the past 20 years at some of the highlights of what has been achieved by the group. This includes discussion of how a small project for Australia Post inspired a stream of research into hub location problems; the development of rostering optimisation methods and the challenges of commercialising it; and the range of interesting OR problems in bulk material (mining) supply chains. The talk will also be used to comment on the developments in OR in Australia more widely and the likely trends into the future.
23 September 2015 - Asef Nazari
Expansions on Land-use Trade-off Optimisation (LUTO)
Abstract: CSIRO has previously developed a model of land-use trade-offs that considers the possible evolution of agricultural land areas in Australia over the next 40 years. This can be modelled as a large scale multi-stage linear programming problem. However, acquiring the expected outcome requires solving the large scale LP problem which takes more than one hour to solve for a single year. In this regard, we developed a combination of aggregation-disaggregation technique with the concept of column generation to solve the large scale LP problem originating from land use management in the Australian agricultural sector in a shorter amount of CPU time. In addition, increasing demand for greener energy alternatives are putting more pressure on the use of agricultural land for not just food productions but also biofuels, carbon sequestration, biodiversity and other non-traditional uses. A key question is how this is going to impact not only the land use but also the agricultural supply chains that process the outputs of the land use. In this talk we also initiate the question of locations of processing centres and land use in an integrated optimisation model. Here we consider in addition the construction of some processing centres for bio-fuel, bio-energy, livestock facilities and so forth, which introduces a new combinatorial aspect to the model. The decisions of land use and the location of processing centres are interlinked as transport costs based on distances are often instrumental in determining the economic viability of some of the land uses and conversely economies of scale are necessary to justify investment in processing plants. We introduce a model containing both problems of a land allocation and a facility location simultaneously which results in a large scale mixed integer linear programming (MILP) problem and therefore is computationally difficult to solve, and we will cover some of the computational difficulties.
Biographical Info: Asef was awarded his PhD in 2009 on the topic of developing derivative free algorithms for non-smooth optimisation problems from the University of Ballarat. Immediately after his PhD, he was appointed as a research associate at the UniSA to conduct research on the optimal expansion of a power system. Since 2013 he has been employed by CSIRO to be involved in several industrial projects
2 September 2015 - Kristian Rotaru
3.30pm, Room 7.84, Building H, Monash Caulfield
Risk information processing and decision-making with strategic performance measurement systems: an eye-tracking study
To address the limitations of the traditional strategic performance measurement systems (SPMSs) in visualizing risk and preventing excessive managerial risk-taking, a number of research studies proposed to extend the functionality of SPMSs by incorporating risk information into traditional SPMSs, such as balanced scorecards. Thus, despite the growing calls of practitioners and researchers on combining performance and risk measures as part of an extended framework, there is a lack of uniform vision about what constitutes such a framework. The aim of this study is to investigate how the representation of risk-related information (characteristics of risk events and key risk indicators) in SPMSs influences the identification and processing of this information in managerial risky decision making. This study benefits from the use of eye-tracking methodology in a laboratory experiment, which allowed to acquire better understanding of the cognitive processes and the subsequent behavioural response associated with managerial risky decision-making when using SPMSs as a tool for decision support.
Dr Kristian Rotaru (PhD in Economics, PhD in Information Systems/Risk Management) works in the domains of risk modelling and decision making. He is a Member of the Editorial Review Board of the Journal of Operations Management. At Monash Business School, Kristian leads the Risk Analysis, Judgement and Decision-Making cross-disciplinary research team that focuses on integration of normative research informed by analytical and simulation modelling methods and descriptive research, informed by laboratory and field experiments. In his research he adopts a variety of research methods, including market data analysis, conceptual, analytical and simulation modelling and laboratory experiments (involving the use of eye-tracking and electroencephalography technology). Kristian lectures Business Analytics, Accounting Information Systems and Financial Modelling units.
19 August 2015 - 2pm - Carleton Coffrin (NICTA)
Carleton gave us an entertaining and insightful presentation about solving the Optimal Power Flow (OPF) problem for AC electrical power networks. In OPF decisions are made about the amount of electricity generation undertaken at generation nodes in a network, so that demand is fulfilled at a series of demand nodes - subject to the non-linear and non-convex constraints relating to AC power flow in transmission networks. There are successive relaxations to the full problem: via semi-definite programming, conical programming, a transport model, and finally a "copper sheet" model without transmission line flow limits. On standard sets of test instances the strongest two relaxations displayed remarkable performance, i.e., finding optimal every time... but further investigation showed that these standard instances were in fact too easy to solve to global optimality, because the data was such that certain sets of constraints would never be active. This led to the development of better benchmark datasets which have proven far more interesting to solve and which are true tests of algorithms for Optimal Power Flow.