  Transportation is an essential area in the nowadays society, both for
business sector and citizenry. There are different kinds of transportation
systems, each one with its own characteristics. In the same way, various areas
of knowledge can deal efficiently with the transport planning. The majority of
the problems related with the transport and logistics have common
characteristics, so they can be modeled as optimization problems, being able to
see them as special cases of other generic problems. These problems fit into
the combinatorial optimization field. Much of the problems of this type have an
exceptional complexity. A great amount of meta-heuristics can be found the
literature, each one with its advantages and disadvantages. Due to the high
complexity of combinatorial optimization problems, there is no technique able
to solve all these problems optimally. This fact makes the fields of
combinatorial optimization and vehicle routing problems be a hot topic of
research. This doctoral thesis will focus its efforts on developing a new
meta-heuristic to solve different kind of vehicle routing problems. The
presented technique offers an added value compared to existing methods, either
in relation to the performance, and the contribution of conceptual originality.
With the aim of validating the proposed model, the results obtained by the
developed meta-heuristic have been compared with the ones obtained by other
four algorithms of similar philosophy. Four well-known routing problems have
been used in this experimentation, as well as two classical combinatorial
optimization problems. In addition to the comparisons based on parameters such
as the mean, or the standard deviation, two different statistical tests have
been carried out. Thanks to these tests it can be affirmed that the proposed
meta-heuristic is competitive in terms of performance and conceptual
originality.
