|• Systems Engineering is a way of thinking that helps engineer sustainable systems, i.e. ones that meet the needs of stakeholders in the short, medium and long terms.
• This course provides an overview of the main principles of Systems Engineering, and includes an introduction to the use of operations research methods in the determination of optimal systems.
|The world’s growing population, changing demographics, and changing climate pose formidable challenges to humanity’s ability to live sustainably. Ensuring that humanity can live sustainably requires accommodating Earth’s growing and changing population through the provision and operation of a sustainable and resilient built environment. This requires ensuring excellent decision-making as to how the built environment is constructed and modified.
The objective of this course is to ensure the best possible decision making when engineering sustainable systems, i.e. ones that meet the needs of stakeholders in the short, medium and long term. In this course, you will learn the main principles of Systems Engineering that can help you from the first idea that a system may not meet expectations, to the quantitative and qualitative evaluation of possible system modifications. Additionally, the course includes an introduction to the use of operations research methods in the determination of optimal solutions in complex systems.
More specifically upon completion of the course, you will have gained insight into:
• how to structure the large amount of information that is often associated with attempting to modify complex systems
• how to set goals and define constraints in the engineering of complex systems
• how to generate possible solutions to complex problems in ways that limit exceedingly narrow thinking
• how to compare multiple possible solutions over time with differences in the temporal distribution of costs and benefits and uncertainty as to what might happen in the future
• how to assess values of benefits to stakeholders that are not in monetary units
• how to assess whether it is worth obtaining more information in determining optimal solution
• how to take a step back from the numbers and qualitatively evaluate the possible solutions in light of the bigger picture
• the basics of operations research and how it can be used to determine optimal solutions to complex problems, including linear, integer and network programming, dealing with multiple objectives and conducting sensitivity analyses.
|The weekly lectures are structured as follows:
1 Introduction – An introduction to System Engineering, a way of thinking that helps to engineer sustainable systems, i.e. ones that meet the needs of stakeholders in the short, medium and long terms. A high-level overview of the main principles of System Engineering. An introduction to the example that we will be working with through most of the course. The expectations of your efforts throughout the semester.
2 Situation analysis – How to structure the large amount of information that is often associated with attempting to modify complex systems.
3 Goals and constraints – How to set goals and constraints to identify the best solutions as clearly as possible.
4 Generation of possible solutions – How to generate possible solutions to problems, considering multiple stakeholders.
5 Analysis – 1/5 – The principles of net-benefit maximization and a series of methods that range from qualitative and approximate to quantitative and exact, including pairwise comparison, elimination, display, weighting, and expected value.
6 Analysis – 2/5 – The idea behind the supply and demand curves and revealed preference methods.
7 Analysis – 3/5 – The concept of equivalence, including the time value of money, interest, life times and terminal values.
8 Analysis – 4/5 – The relationship between net-benefit and the benefit-cost ratio. How incremental cost benefit analysis can be used to determine the maximum net benefit. Marginal rates of return and internal rates of return.
9 Analysis – 5/5 – How to consider multiple possible futures and use simple rules to help pick optimal solutions and to determine the value of more information.
10 Evaluation of solutions – Regardless how sophisticated an analysis is, it requires that decision makers stand back and critically evaluate the results. This week we discuss the aspects of evaluating the results of an analysis.
11 Operations research – 1/4 – Once quantitative analysis is used it becomes possible to use operations research methods to analyse large numbers of possible solutions. This week we discuss linear programming and the simplex method.
12 Operations research – 2/4 – How sensitivity analysis is conducted using linear programming.
13 Operations research – 3/4 – How to use operations research to solve problems that consist of discrete values, as well as how to exploit the structure of networks to find optimal solutions to network problems.
14 Operations research – 4/4 – How to set up and solve problems when there are multiple objectives.
The course uses a combination of qualitative and quantitative approaches. The quantitative analyses requires the use of Excel. An introduction to Excel will be provided in one of the help sessions.
|• The lecture materials consist of a script, the slides and example calculations in Excel.
• The lecture materials will be distributed via Moodle two days before each lecture.
|Appropriate literature in addition to the lecture materials will be handed out when required via Moodle.
|Prerequisites / Notice
|This course has no prerequisites.
|Concepts and Theories
|Techniques and Technologies
|Media and Digital Technologies
|Cooperation and Teamwork
|Leadership and Responsibility
|Self-presentation and Social Influence
|Sensitivity to Diversity
|Adaptability and Flexibility
|Integrity and Work Ethics
|Self-awareness and Self-reflection
|Self-direction and Self-management