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Drinking Water Engineering and Science An interactive open-access journal
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Volume 5, issue 1
Drink. Water Eng. Sci., 5, 31–37, 2012
https://doi.org/10.5194/dwes-5-31-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: CCWI 2011

Drink. Water Eng. Sci., 5, 31–37, 2012
https://doi.org/10.5194/dwes-5-31-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  15 Aug 2012

15 Aug 2012

Robust optimization methodologies for water supply systems design

J. Marques1, M. C. Cunha1, J. Sousa2, and D. Savić3 J. Marques et al.
  • 1Departamento de Engenharia Civil, Faculdade de Ciências e Tecnologia da Universidade de Coimbra, Portugal
  • 2Departamento de Engenharia Civil, Instituto Superior de Engenharia de Coimbra, Portugal
  • 3Centre for Water Systems, School of Engineering, Computing and Mathematics, University of Exeter, UK

Abstract. Water supply systems (WSSs) are vital infrastructures for the well-being of people today. To achieve good customer satisfaction the water supply service must always be able to meet people's needs, in terms of both quantity and quality. But unpredictable extreme conditions can cause severe damage to WSSs and lead to poorer levels of service or even to their failure. Operators dealing with a system's day-to-day operation know that events like burst water mains can compromise the functioning of all or part of a system. To increase a system's reliability, therefore, designs should take into account operating conditions other than normal ones. Recent approaches based on robust optimization can be used to solve optimization problems which involve uncertainty and can find designs which are able to cope with a range of operating conditions. This paper presents a robust optimization model for the optimal design of water supply systems operating under different circumstances. The model presented here uses a hydraulic simulator linked to an optimizer based on a simulated annealing heuristic. The results show that robustness can be included in several ways for varying levels reliability and that it leads to more reliable designs for only small cost increases.

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