TECHNICAL PAPERS
Sep 1, 2006

Optimal Layout of Sewer Systems: A Deterministic versus a Stochastic Model

Publication: Journal of Hydraulic Engineering
Volume 132, Issue 9

Abstract

The optimization of a new or partially existing urban drainage system may be modeled as a subproblems sequence of layout and optimal design within the discrete search space. The design optimization, incorporating the optimal selection of the pumping stations, intermediate manholes, pipe sections, and installation depths, for a general system fixed layout in plan, is a high level sequential decision problem which may be efficiently solved deterministically through a multilevel dynamic programming model. The optimal general layout may be selected in a deterministic way by means of a simple economical comparison of all plan solutions having optimized designs, for small to medium sized systems (if the specific restrictions of the applications are appropriately exploited) in practicable computer time. For larger dimension networks, where it is clearly impossible to achieve plan optimization with full enumeration (which is a NP complete), stochastic search models can be used. For the subproblem layout, an effective enumeration model is presented; the results of a stochastic model proposed previously, using simulated annealing for an application example, are compared and discussed in detail.

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Acknowledgment

The writers are grateful to Professor Luis Miguel Simões for his review and suggestions.

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Published In

Go to Journal of Hydraulic Engineering
Journal of Hydraulic Engineering
Volume 132Issue 9September 2006
Pages: 927 - 943

History

Received: Dec 30, 2003
Accepted: Jul 14, 2005
Published online: Sep 1, 2006
Published in print: Sep 2006

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Authors

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A. Freire Diogo [email protected]
Assistant Professor, Dept. of Civil Engineering, Univ. of Coimbra, Pólo II, 3030-290 Coimbra, Portugal (corresponding author). E-mail: [email protected]
Victor M. Graveto
Chair Professor, Dept. of Civil Engineering, Univ. of Coimbra, Pólo II, 3030-290 Coimbra, Portugal.

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