Short Courses

Short Courses

Teaching Banner

Coming in 2019, the SMART Infrastructure Facility will be offering a range of short courses developed based on our interdisciplinary collaboration in a various research fields including data analytics, economics, system engineering, operation research, transport, water, energy and modelling & simulation.

SMART aims to combine our comprehensive and unique research and project experiences with the knowledge behind it to educate through case studies and live projects. SMART's short courses aim to enhance understanding and application of the relevant theories and technologies.

Upcoming short courses can be found below.

Short Course Facilitators

Infrastructure System of Systems Engineering

Ricardo Peculis
Farid Shirvani
William Scott                                                                     

Participatory Modelling and Serious Gaming

Juan Castilla
Pascal Perez
Shiva Pedram

Computational Methods in Supply Chain and Logistics

Mehrdad Amirghasemi
Johan Barthelemy

Introduction to Agent-Based Modelling of Urban Systems

Johan Barthelemy
Juan Castilla
Nicolas Verstaevel
Pascal Perez

Introduction to Internet of Things

Nicolas Verstaevel
Johan Barthelemy
Pascal Perez

Big Data Analytics and its Application

Jun Ma
Jie (Jack) Yang
Johan Barthelemy
Bo (Bobby) Do

Urban Transport Planning for the Digital Age

Bo (Bobby) Do
Cole Hendrigan
Nicolas Verstaevel

Overviews of SMART's short courses for 2019 can be found below. If you are interested in our short courses or would like further information, you can contact us here. More information and registration details coming soon.

Short Course Overviews


Infrastructure System of Systems Engineering

The course will review SE and present systems theory concepts necessary to address the main topic of engineering Infrastructure SoS. The review of SE and systems theory will be placed within the context of SoS, illustrated with hands on examples extracted from infrastructure systems.

Infrastructure systems are sociotechnical systems within an organisational environment. The presence of social and organisational aspects increases complexity and influence these systems throughout their life cycle, from conception and planning, engineering, operation, upgrades and final disposal. The course will have a strong focus on sociotechnical infrastructure SoS.

Various approaches for SoSE will be presented and discussed. The ‘systems-thinking’, considered the most adequate approach to deal with the complexity of sociotechnical SoS, will be presented and illustrated with practical examples. DANSE (Designing for Adaptability and evolution in System of Systems Engineering) methodology will be introduced.

The course will address the fundamentals of modelling and simulation, considered to be of great importance for SoSE, and will include an introduction to the System Modelling language (SysML), Architecture Framework and Model-Based-Systems Engineering (MBSE) applied to SoS.

 


 

Participatory Modelling and Serious Gaming

Making science interactive, engaging, and effective

Many, if not all, of the social, environmental, infrastructure, and economic systems that we depend on are complex adaptive systems. To understand how these systems work, we often build and operate mental models to represent and understand reality and computer models to simulate their behaviour in time and space. Computer models however become difficult to understand and communicate when they include human decisions.

Participatory Modelling is a flexible and innovative tool for making interdisciplinary models more transparent, usable, and effective in helping stakeholders solve management dilemmas, reach agreement on contentious issues, and balance trade-offs between competing goals. Participatory modelling emphasises the interactive involvement of stakeholders in the learning process about the complex system they are part of.

One of the main goals of Participatory Modelling is to create a process where a range of models, policies, and decisions can be tried and tested, without the risk of making mistakes in the real world. Such processes can engage stakeholders in a way that leads to competent deliberation using the best available science.

This short course will provide researchers, government and industry professionals with the knowledge and tools to conceptualise, design, and facilitate participatory modelling activities. Attendees will be introduced to the role that Participatory Modelling can play in overcoming the curse of interdisciplinarity, knowledge accumulation by experts, bounded rationality, and in enabling stakeholder participation, adaptive management, and consensus building. Students will learn about modelling techniques which can provide a structured process to include the most important aspects of a problem in a coherent and simple but elegant simulation model. The course will be taught as a mixture of lectures and practical work, where students will be given the opportunity to design and facilitate their own participatory modelling activity.

 


 

Computational Methods in Supply Chain and Logistics

Today's world is producing ever increasing amount of data. Business then need data analysis to provide forward-looking guidance that yields better, more-informed decisions. This subject introduces quantitative methods to optimise the decision to be made in the context of supply chain and logistic systems. Each method will be illustrated with real world case studies. As such, the participants will learn to verify and enhance existing operating models.

The course starts with an introduction to supply chain and logistics and, some representative problems with several real-life applications. Effective tools for tackling these problems such as standard mathematical techniques or Linear Programming are explained and their implementation in Microsoft Excel is emphasised. The course is concluded by introducing some advanced metaheuristics, and their implementation in Excel VBA.

 


 

Introduction to Agent-Based Modelling of Urban Systems

 As the social, economic, urban, and infrastructure systems that surround us become more interconnected and complex, our ability to understand them must also. Simple models no longer suffice to answer many of our questions. The advent of widespread fast computation is opening a door for us to work on more complex problems and build and analyse more complex models. This course provides an introduction to one of the primary methodologies for research in the exciting new field of Computational Social Science. Agent-based modelling (ABM) offers a new way of doing science: by conducting computer based experiments. ABM is applicable to complex systems embedded in natural, social, and engineering contexts, across domains that range from engineering to sociology.

The course will offer a comprehensive description of the core concepts, methods, and applications of ABM. Its hands-on approach—exposing students to a number of real-world examples and exercises using the GAMA platform—will enable attendees to begin conceptualising, constructing and analysing their models immediately, regardless of experience or discipline. The course will first describe the nature and rationale of complex adaptive systems, present the methodology for designing and building agent-based simulations that capture the essence of these systems, and finally how to utilise ABMs to answer questions about the complex systems that are embedded in natural, social, and engineering contexts. The course will draw on applications in a wide variety of fields to help illustrate the power of the ABM methodology. Along the way, we will guide you through a series of hands-on examples that will provide the tools and help you understand how you can use ABM to investigate your own questions.

 


 

Introduction to Internet of Things

With more than 30 Billion connected devices expected by 2020, the Internet of Things (IoT) is radically changing the technological landscapes. Applications opportunities are endless: home automation, healthcare, predictive maintenance, agriculture, energy management, or transportation are only some of those use cases. However, the Internet of Things is more than just sensors, it’s a process ranging from remote data collection to data analytics in order to grasp the full potential of your data.

This course offers an introduction to the IoT covering not only the theoretical background and current usages, but also providing practical knowledge through hands-on tutorials and workshops. Students will gain expertise on the whole IoT process.

 


 

Big Data Analytics and its Application

This introductory course on data science covers the following topics: data manipulation, data analysis with statistic and machine learning, data visualization and how to work with large data sets. Those concepts will be illustrated using programming languages often used and freely available, namely R, Python and SQL.

The course presents in a practical way multivariate statistical analysis methods such as Regression, Clustering, Principal Component Analysis, Factor Analysis and ANOVA.

 


 

Urban Transport Planning for the Digital Age

Traditional methods for transport planning have been widely used for the past age, however more and more transport researchers and planners have realised the shortcomings of the classic methods in the digital age where historical and real-time data from various digital sources, such as GPS, smartphones, smart cards and Bluetooth sensors, are more readily available for better transport planning. Moreover, compared to traditional transport modes (e.g. bike, car, bus and train), more options (like autonomous vehicle, electric vehicles, connected vehicle, and scooter) are likely to emerge providing solutions for unsolved problems as well as posing new challenges in planning for their impacts on the demand for urban transport.

It is however necessary to revisit the essentials of urban transport planning to understand the effective use of digital data and new technologies and how they can be used for providing smarter mobility solutions. This short course will provide transport researchers and planners with basic knowledge of transport planning process, as well as major innovations and changes at the digital age.

Real case studies worldwide will be shared with audiences as references for modern urban transport planning. The course will be taught as a mixture of lectures and case studies throughout all three days.

 

Team

Bobby Du Headshot

Dr Bobby Du

Course Coordinator

Email: bo_du@uow.edu.au
Telephone: +61 2 4239 2270

Profile 

Last reviewed: 3 December, 2018