Design Optimization Problem: Coursework Overview

Module Learning Outcomes (MLOs) Assessed by Coursework

1. A systematic understanding of knowledge which is at the forefront of modern engineering design and optimisation
2. Knowledge of conducting essential calculations for reliability driven design and design under uncertainties Intellectual / Professional skills & abilities:
3. Ability to plan design optimisation processes for complex engineering problems and to formulate their corresponding optimisation problem and identifying the best applicable search method
4. Skill of using optimisation methods to find the optimum solution for a given optimisation problem and develop critiques of the methods and the optimality of the solution Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
5. Increase proficiency in applying new design and optimisation techniques towards designing products and processes with improved performance and less environmental impact.

Coursework Overview

Choose a design optimisation problem from the attached list of design problems. Complete the set of tasks as specified in section 3.3 of this assignment brief. Write a report with with a maximum of 15 A4 pages in the main body. Here the main body of the report is defined as the sections from the introduction to conclusion.

The report will be typed using the template provided, with single line spacing, 11pt Calibri (Body) font.

The following tasks are to be completed on the chosen design problem:

• Provide an introduction to the problem selected, the aims and objects of this study and state the relevance in industry.
• Carry out a literature survey on the theory and application relevant to the selected problem.
• Formulate the selected problem verbally and mathematically in the form of an optimisation problem. Elaborate on the identification of design qualities, selection of design objective(s),design variables, type of constraints and type of the optimisation problem.
• Select an apporpriate optimization method (Genetic Algorithm, Particle Swarm or Simulated Annealing) for solving the optimi-sation problem. Justify the rationale behind the selected method and discuss the selected constraint handling method, fitness definition, and objective evaluation.
• Implement the problem formulation in the optimization code and briefly discuss the implementation in the main body. Provide the MATLAB code in Appendix A.
• Solve the optimisation problem and prove the optimality of the solution. Investigate effect of changes in the controlling parameters.
• Identify all sources of uncertainties in the selected design optimisation problem.
• Quantify/Approximate the level and distribution of uncertainties for each uncertain parameter identified above.
• Write a Monte Carlo code to investigate the effect of uncertainties on the performance and the robustness of the optimal solution found above. Provide the MATLAB code in Appendix B.
• Conclude the report with the major findings.

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Select an apporpriate optimization method (Genetic Algorithm, Particle Swarm or Simulated Annealing) for solving the optimi-sation problem. Justify the rationale behind the selected method and discuss the selected constraint handling method, fitness definition, and objective evaluation.

Implementation Implement the problem formulation in the optimization code and briefly discuss the implementation in the main body. Provide the MATLAB code in Appendix A.

Solution Solve the optimisation problem and prove the optimality of the solution. Investigate effect of changes in the controlling parameters.

Nondeterministic Analysis Uncertainty Selection Identify all sources of uncertainties in the selected design optimisation problem.

Quantify/Approximate the level and distribution of uncertainties for each uncertain parameter identified above.