49928 Design Optimisation for Manufacturing
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Credit points: 6 cp
Subject level:
Postgraduate
Result type: Grade and marksRequisite(s): 120 credit points of completed study in spk(s): C10061 Bachelor of Engineering Diploma Engineering Practice OR 120 credit points of completed study in spk(s): C10066 Bachelor of Engineering Science OR 120 credit points of completed study in spk(s): C10067 Bachelor of Engineering OR 120 credit points of completed study in spk(s): C09067 Bachelor of Engineering (Honours) Diploma Professional Engineering Practice OR 120 credit points of completed study in spk(s): C09066 Bachelor of Engineering (Honours)
These requisites may not apply to students in certain courses. See access conditions.
Recommended studies: knowledge of computer programming
Description
The increasing demand on engineers to make their 'best' possible decisions in product design and manufacturing process at decreasing costs and a faster pace requires knowledge of methods in design optimisation. Optimisation has become a necessary part of product design and decision-making activities in mechanical, manufacturing and mechatronic engineering. This subject emphasises applications of advanced optimisation techniques in product design, manufacturing and project planning. It introduces students to an array of optimisation techniques and enables students to learn to use advanced techniques applicable in solving real product design and manufacturing problems such as machine scheduling, flexible assembly system scheduling, supply chain planning, job shop scheduling, project planning and scheduling, etc. On successful completion of this subject, students are able to understand the fundamentals of optimisation techniques and apply appropriate optimisation techniques in various applications.
Subject learning objectives (SLOs)
Upon successful completion of this subject students should be able to:
| 1. | Identify the fundamentals of optimisation methods and their applications to manufacturing process and product design. (C.1) |
|---|---|
| 2. | Construct optimisation models including design objectives, constraints and variables, design constraints and design variables. (C.1) |
| 3. | Apply appropriate optimisation techniques programs.(D.1) |
| 4. | Interpret and understand the limitations of solutions obtained from optimisation. (D.1) |
| 5. | Use optimal design tools/software to solve optimisation problems. (C.1) |
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):
- Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
- Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)
Contribution to the development of graduate attributes
Engineers Australia Stage 1 Competencies
Students enrolled in the Master of Professional Engineering should note that this subject contributes to the development of the following Engineers Australia Stage 1 competencies:
- 1.3. In-depth understanding of specialist bodies of knowledge within the engineering discipline.
- 2.1. Application of established engineering methods to complex engineering problem solving.
- 2.2. Fluent application of engineering techniques, tools and resources.
Teaching and learning strategies
The teaching and learning approaches generally consist of brief lectures followed by class activities. The students are expected to go through online materials and recommened videos before coming to the class so that they are well prepared for the class activities. Some graphical and visualisation optimisation programs are developed to assist student learning and provide students with optimisation tools. Students are encouraged to use the practical problems from their work and research as project problems in order to maximise their study outcomes.
Content (topics)
The subject is divided into five components: construction of product design and manufacturing process models, graphical optimisation, linear programming, nonlinear programming, discrete optimisation and computational intelligence. The theory underlying various optimisation methods is covered. The emphasis is on application of various optimisation methods in product design, manufacturing process, mechatronic system design and project management, including machine scheduling, flexible assembly system scheduling, supply chain planning, job shop scheduling, project planning and scheduling, etc.
Assessment
Assessment task 1: Assignment 1: Modelling of optimisation problems and graphical optimisation
| Intent: | Modelling of optimisation problems and graphical optimisation |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Exercises |
| Groupwork: | Individual |
| Weight: | 10% |
Assessment task 2: Project 1: Linear programming and its applications
| Intent: | Linear programming and its applications |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Project |
| Groupwork: | Group, group and individually assessed |
| Weight: | 20% |
Assessment task 3: Project 2: Nonlinear programming and its applications
| Intent: | Nonlinear programming and its applications |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Project |
| Groupwork: | Group, group and individually assessed |
| Weight: | 20% |
Assessment task 4: Assignment 2: Advanced optimisation techniques and applications
| Intent: | Advanced optimisation techniques and applications. |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 3, 4 and 5 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Report |
| Groupwork: | Individual |
| Weight: | 20% |
Assessment task 5: Exam
| Intent: | To assess the students' understanding and the ability to use the different optimisation techniques learned from the subject |
|---|---|
| Objective(s): | This assessment task addresses the following subject learning objectives (SLOs): 1, 2, 3 and 4 This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs): C.1 and D.1 |
| Type: | Examination |
| Groupwork: | Individual |
| Weight: | 30% |
| Length: | 2 hours plus 10 minutes reading time |
Minimum requirements
In order to pass the subject, a student must achieve an overall mark of 50% or more.
Recommended texts
Venkataraman, P (2002), “Applied Optimization with Matlab Programming”, John Wiley & Sons
References
1. Pinedo, M.L. (2005), “Planning and Scheduling in Manufacturing and Services”, Springer
2. Saravnan, R. (2006), “Manufacturing Optimization through Intelligent Techniques (Manufacturing Engineering and Materials Processing)”, CRC Press.
3. “Optimization Methods for Manufacturing”, Edited by Leondes, C. (2001), CRC Press.
4. “Computational Intelligence in Manufacturing Handbook”, Edited by Jun Wang et al (2001), CRC Press.
5. “Artificial Intelligence and Robotics in Manufacturing”, Edited by Leondes, C. (2001), CRC Press.
6. Papalambros, P.Y. and Wilde, D.J. (2000), “Principles of Optimal Design”, Cambridge
7. Ansari, N., and E. Hou (1997), “Computational Intelligence for Optimization”, Kluwer, Boston
8. Belegundu, A.D., and Chandrupatla, T.R. (1999), “Optimization Concepts and Applications in Engineering”, Prentice-Hall, Upper Saddle River, New Jersey
9. Jones, C.V. (1996), “Visualization and Optimization”, Kluwer Academic Publishers, Norwell, Massachusetts
10.http://commerce.concordia.ca/bourjolly/lp.html. An interactive introduction to Linear Programming
11. http://www.cs.pitt.edu/~kirk/algorithmcourses/index.html, Optimization algorithms courses on the internet
12. http://www.maximal-usa.com/mpltutor/TutorToc.html On-line tutorial to teach real-world optimization modeling techniques using the MPL Modeling Language.
13. http://web.mit.edu/jorlin/www/15.082/15082_syllabus_2003.html, a course in Network Optimization
14. http://www.math.washington.edu/~korf/classes/517/517.html. Optimization under uncertainty
15. http://www.statslab.cam.ac.uk/~rrw1/oc/index.html. Optimization and control
16. http://www.statslab.cam.ac.uk/~rrw1/opt/index98.html
17. http://www.informs.org/Resources/Courses/. Optimization resource collection:
18. http://carbon.cudenver.edu/~hgreenbe/courseware/LPshort/intro.html. A Short Course in Linear Programming
19. http://web.mit.edu/jorlin/www/15.053/optimization_sites.html
20. http://ocw.mit.edu/OcwWeb/Sloan-School-of-Management/15-053Introduction-to-OptimizationSpring2002/CourseHome/index.htm. MIT OpenCourseWare