ASOR Seminar Schedule

ASOR's Thursday Afternoon Online Seminars run weekly at 4.30pm AEST or AEDST (4pm SA, 1.30pm or 2.30pm WA depending on daylight saving, etc) using Zoom. They are open to members and non-members, 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 record these sessions with the presenters' permission and post them on our vimeo account with links from this page (note that it can take us several months to edit and approve the recordings, and even longer sometimes, sorry!).

2024 Series

2024 Seminar #1
8 February 2024
Richard Watson (Ryan Watson Consulting P/L)
A Geospatial Data Analysis of Environmental Factors in Victorian Road Accidents

2024 Seminar #2
7 March 2024
Simon Dunstall and Simon Knapp (CSIRO Data61)
A simulation-optimisation system for assisting bushfire firefighting fleet decisions

2023 Series

2023 Seminar #1
20 July
Asef Nazari (Deakin University)
Metaheuristics for Optimising and Balancing Assembly Lines

2023 Seminar #2
3 August 2023
Honglei Xu (Curtin University)
Two-stage Games Under Uncertainty: Mathematical Formulation and Computation

2023 Seminar #3
17 August 2023
Kyle Harrison (ASOR Rising Star Award recipient 2022) Surrogate-Assisted Analysis of the Parameter Configuration Landscape for Meta-heuristic Optimisation
Real-world optimisation problems are often too complex for exact solution methodologies to address in a reasonable amount of time. Meta-heuristic optimisers can provide high-quality solutions to challenging problems in a reasonable amount of time but are highly sensitive to the values assigned to their control parameters. In fact, tuning control parameter values of a meta-heuristic can be thought of as an optimisation problem in itself. Thus, a valid question to ask is: how can we efficiently optimise the optimiser? Towards this goal, this talk will discuss the usage of artificial neural networks (ANNs) as surrogate models to greatly reduce the computational burden associated with characterising the parameter configuration landscape (PCL) of meta-heuristic optimisers. The trained surrogate models allow for constant-time estimation of the fitness associated with multiple executions of a parameter configuration, thereby facilitating an efficient way to sample and evaluate a large volume of parameter configurations. Ultimately, this can be used to design more effective parameter control strategies, which can then be used to improve optimisation outcomes.

2023 Seminar #4
31 August 2023
Sameh Tawfiq AlShihabi (U. Sharjah)
A Novel Core-Based Optimization Framework for Binary Integer Programs- the Multidemand Multidimensional Knapsack Problem as a Test Problem

2023 Seminar #5
14 September 2023
Yunzhuang Shen
Solution prediction via machine learning for combinatorial optimization

2023 Seminar #6
26 October 2023
Nariman Mahdavi Mazdeh (CSIRO Energy, Newcastle)
Data-enabled Predictive Control (DeePC) of Buildings
An overview of the state-of-the-art data-driven control technique, the main motivations, and the similarities and differences with model-based control techniques, such as Model Predictive Control (MPC). DeePC is a recently developed approach, inspired by behavioural system theory, that combines system identification, estimation, and control in a single optimisation problem, for which only historical input/output observations of the system is required. Addiitionally, some insights about the DeePC application in building control, plus some initial simulation results after using the Building Optimisation Testing Framework.

2022 Series

2022 Seminar #1
18 August
Dr. Kyle Harrison (University of New South Wales (UNSW) Canberra)
Project Portfolio Selection and Scheduling for Future Force Design
 
Abstract: A common problem faced by organizations is how to select and schedule an optimal portfolio of projects subject to various operational constraints, such as a limited budget. This problem is known as the project portfolio selection and scheduling problem (PPSSP). In the context of defence, the PPSSP arises as part of the future force design (FFD) planning task, where the objective is to maximise the delivery of defence capabilities through project selection and scheduling. However, addressing the PPSSP in the context of defence has its own unique challenges and nuances, such as long planning horizons and limited availability of (public) data. This talk will discuss the development of two models for the PPSSP, namely a project-oriented model and an option-oriented model, with a focus on how they can be used to address the FFD task. This talk will also briefly discuss the use of meta-heuristic optimisers to provide solutions to the proposed models.
 
Biography: Kyle is a Research Associate with the School of Engineering and Information Technology at the University of New South Wales (UNSW) Canberra, Australia. Previously, he was a Postdoctoral Fellow at the University of Ontario Institute of Technology (Ontario Tech University), Canada. He received his PhD in Computer Science from the University of Pretoria, South Africa, in 2018, and the M.Sc. and B.Sc. degrees in Computer Science from Brock University, Canada, in 2014 and 2012, respectively. His research interests include computational intelligence, self-adaptive optimisation, fitness landscape analysis, operations research, and real-world applications of complex networks. He has co-authored numerous publications in top-tier journals and conferences and serves as an Editor for the journal Engineering Applications of Artificial Intelligence.

2022 Seminar #2
25 August
Dr Reena Kapoor (CSIRO Data61)
Optimisation and Movement Analytics

Presenting findings from a major literature view just completed by a CSIRO team into questions associated with extracting information and insight from movements of people and objects captured by tracking systems in industrial, agricultural and environmental contexts.

2022 Seminar #3
15 September
Simon Dunstall (CSIRO Data61)
Managing wildfire risk for agricultural and horticultural regions

Wildfires (bushfires and grassfires) can destroy crops, livestock, farm infrastructure and the lives and livelihoods of individuals, businesses and communities impacted by a fire event. In this context it is natural to look to methods that can be used to reduce fire occurrence, the chances of fire escalating to major events, and/or means of halting fire progress so as to protect agricultural lands and/or key supply chain assets. From an OR perspective, these are analytics and resource-constrained optimisation questions.

2022 Seminar #4
29 September
Dr. Sanath Kahagalage (Capability Systems Centre, UNSW Canberra)
An application of exploratory modelling and analysis in defence resource planning and asset management under deep uncertainty

Abstract: Budget constraints, conflicting stakes, and political situations make decision-making a difficult task even under the most favourable circumstances. Therefore, every reduction in uncertainty is more than welcome.  On the other hand, substituting assumptions for deep uncertainties might simplify choices in the short term, but the consequences may come at a much higher price in the longer term. As decisions that ignore deep uncertainty ignore reality, this questions the reliability and effectiveness of actions developed using such approaches. Moreover, the handling of the uncertainty by decision-makers, including risk managers, is in question. We show the consequences of ignoring uncertainties by using the concepts from Robust Decision-Making (RDM).  The concepts from Behaviour-Based Scenario Discovery (BBSD) and Feasible Scenario Space (FSS) are also utilized to show the importance of giving a complete picture to the decision-maker.

Biography: Sanath Kahagalage is a Research Associate at the Capability Systems Centre, UNSW Canberra. He is an honorary fellow in the University of Melbourne. He obtained his PhD from the University of Melbourne and MSc in applied mathematics at Texas Tech university.

2022 Seminar #5
6 October
Dr. Daniel Reich (Naval Postgraduate School, USA)
Using Machine Learning to Improve Public Reporting on U.S. Government Contracts

Abstract: The U.S. Government procures more than $500 billion annually in goods and services on public contracts, which it classifies using a hierarchical product and service taxonomy. Classification serves several purposes including transparency in the use of taxpayer funding, reporting, tracing and segmenting government expenditures, budgeting and forecasting. Government acquisition personnel have historically performed these classifications manually, resulting in a process that is time-consuming, error-prone and offers limited visibility into government purchases. Using almost 4 million historical data records on governmental purchases, we improve classification by leveraging natural language processing and machine learning techniques to generate a descriptive manual and a predictive classifier. Our machine learning models are embedded in multiple software applications, including a web application we developed, used by government personnel and other contracting professionals.

Biography: Daniel Reich joined the Naval Postgraduate School in Monterey, California in 2018 after holding several positions at Ford Motor Company, ranging from research to management. He was nominated for a rotational leadership program, through which he served in managerial roles in manufacturing, marketing, product planning, and information technology. Daniel holds a BS in applied mathematics from Columbia University in New York and a PhD in applied mathematics from the University of Arizona in Tucson.

2022 Seminar #6
13 October
Dr. Yuan Sun (School of Computing and Information Systems, The University of Melbourne)
Problem reduction based on machine learning for combinatorial optimisation

Abstract: In the big data era, the size of many real-world combinatorial optimisation problems has increased significantly over the years, making the problems very hard to solve. The traditional problem reduction methods are designed manually, relying on the intuition or insights of an expert. In this talk, I will introduce innovative machine learning models to automate the process of problem reduction. These machine learning models are trained on easy problem instances for which the optimal solution is known and predict for an unseen problem instance which decision variables can be eliminated without significantly impacting solution quality. I will show that this approach is effective on several classical combinatorial optimisation problems.

Biography: Yuan Sun is a Research Fellow in the School of Computing and Information Systems, The University of Melbourne. He received his PhD degree from The University of Melbourne and a Bachelor’s degree from Peking University. His research expertise is in artificial intelligence, machine learning, operations research, and evolutionary computation. His recent work focuses on developing effective machine learning techniques for solving large-scale combinatorial optimisation problems.

2022 Seminar #7
20 October
Prof Sardar Islam (Victoria University, Australia)
Operations Research methods for computer science research and applications

Prof Sardar Islam is a Distinguished Visiting Professor of Artificial Intelligence at Victoria Univerity's Institute for Sustainable Industries and Liveable Cities. Prior to this he was a Professor of Business, Economics and Finance. He has published extensively across a broad range of disciplines, in leading international journals including Journal of Optimisation Theory and Applications, Annals of Operations Research and Applied Mathematical Modelling. He has also published a number of research books in artificial intelligence and digitalisation, game theory, operations research, supply chian management, accounting, finance, business, economics and law.

APORS INTERNATIONAL CONFERENCE ON 9-11 NOVEMBER - NO SEMINAR

2022 Seminar #8
17 November
Dr. Firouzeh Taghikhah (Univ. Sydney Business School)
Integrated modeling of extended agro-food supply chains: A systems approach

The current intense food production-consumption is one of the main sources of environmental pollution and contributes to anthropogenic greenhouse gas emissions. Organic farming is a potential way to reduce environmental impacts by excluding synthetic pesticides and fertilizers from the process. Despite ecological benefits, it is unlikely that conversion to organic can be financially viable for farmers, without additional support and incentives from consumers. This study models the interplay between consumer preferences and socio-environmental issues related to agriculture and food production. We operationalize the novel concept of extended agro-food supply chain and simulate adaptive behavior of farmers, food processors, retailers, and customers. Not only the operational factors (e.g., price, quantity, and lead time), but also the behavioral factors (e.g., attitude, perceived control, social norms, habits, and personal goals) of the food suppliers and consumers are considered in order to foster organic farming. We propose an integrated approach combining agent-based, discrete-event, and system dynamics modeling for a case of wine supply chain. In practice, our proposed model may serve as a decision-support tool to guide evidence-based policymaking in the food and agriculture sector.
 
Firouzeh Taghikhah is a lecturer in the Discipline of Business Analytics at the University of Sydney Business School. Her research interests are at the interface of computational/complex systems modeling, artificial intelligence, and socio-environmental science to guide evidence-based policymaking.

2022 Seminar #9
24 November
Canchen Jiang (Monash University)
Value stacking of EV participation in the power energy system

The wide adoption of electric vehicles (EVs) enables technologies of vehicle-to-home (V2H), vehicle-to-grid (V2G), and energy trading among EVs in the distribution network. The coordination of EVs can provide value to the grid and generate benefits for EVs but is subject to local network constraints. This work develops a value-stacking optimisation problem maintaining local network constraints to maximise the value of EVs, considering deterministic and stochastic scenarios. In the deterministic scenario, we assume that we can perfectly forecast all parameters in the energy system, such as solar PV generation and load demand consumption. We optimise EV scheduling, especially discharging, to leverage the multiple value streams, including V2G, V2H, and energy trading among EVs, to minimise the cost of prosumers' daily energy usage. The simulation results demonstrate that our value-stacking model achieves significant cost reductions in Australia's National Electricity Market (NEM), ISO New England (ISO-NE), and New York ISO (NY-ISO) in the US. For the stochastic scenario, in the initial step, we develop a multi-stage stochastic optimization model to improve the decision-making process of value-stacking under load demand uncertainty. Additionally, we utilise the Stochastic Dual Dynamic Programming (SDDP) algorithm to calculate the optimal value-stacking profile for minimising all prosumers' daily operation costs. In the future, we will consider EV arrival and departure time as uncertainties in the energy system.

2022 Seminar #10
1 December
Qian Wan (CSIRO Data61)
The agricultural spraying vehicle routing problem with splittable edge demands

The capacitated arc routing problem (CARP) is to find a set of least-cost routes for a fleet of identical vehicles of limited capacity that must service the demand of a subset of edges in a network. We present a splittable agricultural chemical sprayed vehicle routing problem and formulate it as a mixed integer linear program. The main difference is that our problem allows us to split the demand on a single demand edge amongst robotics sprayers. We use theoretical insights about the optimal solution structure to improve the formulation and provide two different formulations of the splittable capacitated arc routing problem (SCARP), a basic spray formulation and a high-edge demands formulation. The solution methods consisting of lazy constraints, symmetry elimination constraints, and a heuristic repair method. Computational experiments on a set of valuable data based on the properties of real-world orchards reveal that the proposed methods can solve the SCARP with different properties. We also report computational results on classical benchmark sets from previous CARP literature. The tested results indicated that the SCARP model could provide cheaper solutions in some instances when compared with the CARP literature. Besides, the heuristic repair method significantly improves the quality of the solution by decreasing the upper bound when solving large-scale problems

2021 Series

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
Harry Gielewski
Validation to Manage Model Risk

12 August 2021
Phil Kilby (CSIRO Data61), reporting on joint work with Dan Popescu and Steven Edwards
Finding solutions to a 3D packing problem arising in logistics. Phil Kilby, reporting on joint work with Dan Popescu (Data61) and Steven Edwards (then with Data61, now with Gurobi)

Packing problems have been widely studied in O.R - from cutting carpets to filling knapsacks. Given that, it is surprising how little work has been done on how to fill a truck. We will describe the classic 3D packing problem, and a restriction we have been investigating that arises as a subproblem when solving problems in logistics. While this is still early work, the methods used may be of interest to others.

26 August 2021
Dr. Marcella Bernardo, U. Wollongong.
Bi-Objective Optimization Model for the Heterogeneous Dynamic Dial-a-Ride Problem with No Rejects

This work proposes a bi-objective mathematical optimization model and a two-stage heuristic for a real-world application of the heterogeneous Dynamic Dial-a-Ride Problem with no rejects, i.e., a patient transportation system. The problem consists of calculating route plans to meet a set of transportation requests by using a given heterogeneous vehicle fleet. These transportation requests can be either static or dynamic, and all of them must be attended to.

9 September 2021
Simon Dunstall, CSIRO
Optimising power system shutdown criteria to reduce wildfire risk

Electricity distribution businesses in bushfire prone areas have to manage their networks carefully in order to reduce their chances of igniting major fires. Turning off the electrical supply when the fire weather conditions are most dangerous is one of the risk reduction methods at their disposal. Doing so is an action that comes at substantial cost for the community and which also introduces new risks, so it is a tool to be used intelligently and rather sparingly. Data science can be used to quantify the costs and benefits at particular times at specific parts of the network, and optimisation can be used to select the conditions under which the power system is preemptively shut down. The development of these mathematical approaches and their application to statewide electrical distribution networks is the subject of this seminar.

23 September 2021
Asghar Moeini, 2020 ASOR Rising Star Award Recipient
The Sparse Travelling Salesman Problem

7 October 2021
Juan Calle Salazar, Deakin University
An optimisation journey. The story of Tactix, an optimisation platform to support strategical-tactical decisions at TDM Transportes.

Each optimisation project has a story behind it. This presentation shares the story behind Tactix, a platform built to support the strategical and tactical decisions at TDM Transportes, an innovative logistic service provider in Colombia. At the heart of Tactix, there is a powerful space-time network framework that supports the formulation of an optimisation model. The model captures the most important details of TDM transport operations and delivers important insights to the TDM operations team. Some of the insights the model has provided have been counter-intuitive, and this presentation will share a case in which a customer that seemed to be very profitable in the network, in fact was not.

Juan Calle is Ph.D. (c) at Deakin University. Before pursuing his Ph.D., he was a Senior Operations Research Analyst at TDM Transportes, a Colombian based Transportation Company. Previously, Juan worked at Decisionware, a Colombian based mathematical programming company. Juan has taught Operations Research courses in leading Latin American Universities. He has a bachelor’s degree in Industrial Engineering and a master’s in Systems Engineering from the Universidad Nacional de Colombia. He is the cofounder of UNGIDO, the Operation Research Group at the National University of Colombia, and ASOCIO, the Colombian Operations Research Association.

21 October 2021
John Hearne, ASOR Ren Potts Medal recipient 2020
The reserve design problem under climate change

SERIES TWO

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

SERIES ONE

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
Open discussion

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.