Bi-objective design-for-control of water distribution networks with global bounds

Autor: Ivan Stoianov, Aly-Joy Ulusoy, Filippo Pecci
Přispěvatelé: Engineering & Physical Science Research Council (E
Rok vydání: 2021
Předmět:
Mathematics
Interdisciplinary Applications
Technology
Operations Research
Mathematical optimization
Class (set theory)
Control and Optimization
Optimization problem
Bi-objective programming
Computer science
Computation
0208 environmental biotechnology
0211 other engineering and technologies
Engineering
Multidisciplinary
Aerospace Engineering
02 engineering and technology
Subset and superset
09 Engineering
Set (abstract data type)
Engineering
Electrical and Electronic Engineering
Resilience (network)
Global optimization
01 Mathematical Sciences
Civil and Structural Engineering
Sequence
Science & Technology
021103 operations research
Operations Research & Management Science
Mechanical Engineering
Integer programming
Water distribution network
020801 environmental engineering
Physical Sciences
Branch and bound
Mathematics
Software
Zdroj: Optimization and Engineering. 23:527-577
ISSN: 1573-2924
1389-4420
DOI: 10.1007/s11081-021-09598-z
Popis: This manuscript investigates the design-for-control (DfC) problem of minimizing pressure induced leakage and maximizing resilience in existing water distribution networks. The problem consists in simultaneously selecting locations for the installation of new valves and/or pipes, and optimizing valve control settings. This results in a challenging optimization problem belonging to the class of non-convex bi-objective mixed-integer non-linear programs (BOMINLP). In this manuscript, we propose and investigate a method to approximate the non-dominated set of the DfC problem with guarantees of global non-dominance. The BOMINLP is first scalarized using the method of $$\epsilon $$ ϵ -constraints. Feasible solutions with global optimality bounds are then computed for the resulting sequence of single-objective mixed-integer non-linear programs, using a tailored spatial branch-and-bound (sBB) method. In particular, we propose an equivalent reformulation of the non-linear resilience objective function to enable the computation of global optimality bounds. We show that our approach returns a set of potentially non-dominated solutions along with guarantees of their non-dominance in the form of a superset of the true non-dominated set of the BOMINLP. Finally, we evaluate the method on two case study networks and show that the tailored sBB method outperforms state-of-the-art global optimization solvers.
Databáze: OpenAIRE
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