1. ZEIT4700 – S1, 2015 Mathematical Modeling and Optimization School of Engineering and Information Technology

2. Deterministic v/s Robust optimization Deterministic : Each evaluation is considered accurate, each variable value is exactly attainable. - Best but (may be) unreliable. Robust : Optimization in presence of uncertainties in variables or objectives, and noise factors - May not be global best, but reliable under given uncertainty. Deterministic and Robust optima may not coincide.

3. Robustness Feasibility robustness : Robustness with respect to constraint violation. (also called Reliability Robustness) Performance robustness : Robustness with respect to the given objective value.

4. Robust optimization 1.Formulation – (add / modify objectives/constraints) 2.Quantification of Robustness 3.Search techniques

5. Six – sigma robust measure

6. Project 2 Identify an optimization problem of your interest. Develop a mathematical model and solve it. Present your formulation and results in a report. Submit the report, code and results. You should clearly indicate the variables and their bounds, expressions for objective and constraint functions and all parameter settings used to solve the problem. Complete analysis and justifications of your choice should be presented in the report. Take note the problem cannot be a textbook problem and has to be your own. Please seek advice on the suitability of your chosen problem with me first. Due date : May 20, 2014

7. Resources Course material and suggested reading can be accessed at http://seit.unsw.adfa.edu.au/research/sites/mdo/ Hemant/design-2.html http://seit.unsw.adfa.edu.au/research/sites/mdo/ Hemant/design-2.html