Many real life problems in the hydraulic engineering literature can be modelled
as constrained optimisation problems. Often, they are addressed in the literature
through genetic algorithms, although other techniques have been proposed. In
this thesis, we address two of these real life problems through a variety of techniques
taken from the Artificial Intelligence and Operations Research areas, such
as mixed-integer linear programming, logic programming, genetic algorithms and
path relinking, together with hybridization amongst these technologies and with
hydraulic simulators. For the first time, an Answer Set Programming formulation
of hydroinformatics problems is proposed.
The two real life problems addressed hereby are the optimisation of the response
in case of contamination events, and the optimisation of the positioning of
the isolation valves.
The constraints of the former describe the feasible region of the Multiple Travelling
Salesman Problem, while the objective function is computed by a hydraulic
simulator. A simulation–optimisation approach based on Genetic Algorithms,
mathematical programming, and Path Relinking, and a thorough experimental
analysis are discussed hereby.
The constraints of the latter problem describe a graph partitioning enriched
with a maximum flow, and it is a new variant of the common graph partitioning.
A new mathematical model plus a new formalization in logic programming are
discussed in this work. In particular, the technologies adopted are mixed-integer
linear programming and Answer Set Programming.
Addressing these two real applications in hydraulic engineering as constrained
optimisation problems has allowed for i) computing applicable solutions to the
real case, ii) computing better solutions than the ones proposed in the hydraulic
literature, iii) exploiting graph theory for modellization and solving purposes,
iv) solving the problems by well suited technologies in Operations Research and
Artificial Intelligence, and v) designing new integrated and hybrid architectures
for a more effective solving