PhD Defence Marquez Calvo

  • 15 Jan 2020
  • 14:30 - 16:30
  • Mekelweg 5, 2628 CD Delft
Western Europe
Auditorium of TU Delft, room: 'Senaatszaal'

On Wednesday 15 January 2020, the Public Defence and Awarding Ceremony of Mr Oscar Marquez Calvo, from Mexico, will take place. Professor Dimitri Solomatine is his promotor and Dr Leonardo Alfonso Segura his co-promotor.

The title of his thesis is

Advancing robust multi-objective optimisation applied to complex model-based water-related problems

14:30 - 14:45               Presentation in the Auditorium of TU Delft, room: 'Senaatszaal'

15:00 - 16:30               Public Defence and Awarding Ceremony

17:00 - 18:00               Drinks in the IHE Restaurant

Summary

The exercise of solving engineering problems that require optimisation procedures can be seriously affected by uncertain variables, resulting in potential underperforming solutions. Although this is a well-known problem, important knowledge gaps are still to be addressed. For example, concepts of robustness largely differ from study to study, robust solutions are generally provided with limited information about their uncertainty, and robust optimisation is difficult to apply as it is a computationally demanding task. The proposed research aims to address the mentioned challenges and focuses on robust optimisation of multiple objectives and multiple sources of probabilistically described uncertainty. This is done by the development of the Robust Optimisation and Probabilistic Analysis of Robustness algorithm (ROPAR), which integrates widely accepted robustness metrics into a single flexible framework. In this thesis, ROPAR is not only tested in benchmark functions, but also in engineering problems related to the water sector, in particular the design of urban drainage and water distribution systems. ROPAR allows for employing practically any existing multi-objective optimisation algorithm as its internal optimisation engine, which enables its applicability to other problems as well. Additionally, ROPAR can be straightforwardly parallelized, allowing for fast availability of results.

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