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International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 1, Nº 1.
Abstract — This paper aims to give a presentation of the PhD
defended by Eneko Osaba on November 16th, 2015, at the
University of Deusto. The thesis can be placed in the field of
artificial intelligence. Specifically, it is related with multi-
population meta-heuristics for solving vehicle routing problems.
The dissertation was held in the main auditorium of the
University, in a publicly open presentation. After the
presentation, Eneko was awarded with the highest grade (cum
laude). Additionally, Eneko obtained the PhD obtaining award
granted by the Basque Government through.
Keywords — Meta-heuristics, Transportation, Vehicle
Routing Problem, Traveling Salesman Problem, Golden Ball
I. INTRODUCTION
On November 16th, 2015, Eneko Osaba defended his PhD
thesis related with multi-population meta-heuristics for
solving vehicle routing problems, at the University of Deusto
[1]. The dissertation was held in the main auditorium of the
University. After the presentation, and the corresponding
doubts and questions raised by the assessing committee,
Eneko was awarded with the highest grade (cum laude).
The thesis was supervised by Dr. Fernando Diaz, associate
professor in the University of Deusto. On the other hand, the
assessing committee of the PhD dissertation was composed
of Professor Mario Piattini (University of Castilla-La
Mancha), Dr. Esther Alvarez (University of Deusto), and Dr.
Xin-She Yang (University of Middlesex, London, UK).
The development of the thesis was funded by the Basque
Government, thanks to a grant awarded through a
competitive process by the education, language policy and
culture department (Grant ID: BFI. 2011-56). It is also
important to highlight that the thesis has the International
Mention, and that Eneko obtained the PhD obtaining award
granted by the Basque Government through a competitive
process.
The main publications associated with the PhD thesis are
[2-5]. The full text of the thesis, the main papers published,
and the source code of the developed method are available on
the personal webpage associated with the University of
Deusto1.
II.SUMMARY OF THE PERFORMED WORK
Transportation is an essential area in the nowadays society.
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.
1http://paginaspersonales.deusto.es/e.osaba.
Concretely, the thesis is focused in the area of artificial
intelligence and vehicle routing problems.
The majority of the problems related with the transport and
logistics have common characteristics. This means that they
can be modeled as optimization problems, being able to see
them as special cases of other generic problems. Much of the
problems of this type have an exceptional complexity,
requiring the employment of techniques for its treatment.
There are different sorts of these methods. Specifically, the
thesis centers its attention on meta-heuristics.
A great amount of meta-heuristics can be found it the
literature. Anyway, due to the high complexity of the
problems, there is no technique able to solve all these
problems optimally. This fact makes the field of vehicle
routing problems be a hot topic of research. For this reason,
the thesis focuses 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. Specifically, the
designed meta-heuristic has been called Golden-Ball (GB),
and it is based in soccer concepts [2, 3].
The main characteristics of the GB can be summarized as
follows. The GB is a multiple-population based meta-
heuristic. First, the whole population of solutions (called
players) is randomly created. Then, these created players are
randomly divided among a fixed number of subpopulations
(called team). Each team has its own training method (or
coach), which is randomly assigned in this first phase. This
training is the way in which each player in the team
individually evolves along the execution. Another important
training is the called Custom Training. In these trainings, a
player which is trapped in a local optimum receives a special
training in cooperation with the best player of its team. Once
this first phase is finished, the competition phase starts. This
second step is divided in seasons, composed by weeks. All
the teams train independently every week, and they face each
other creating a competition league. At the end of every
season, a transfer procedure takes place. In this procedure the
players and coaches can switch teams. The competition phase
is repeated until the termination criterion is reached.
With the aim of validating the proposed model, its
obtained results were compared with the ones obtained by
other four algorithms of similar philosophy. Additionally,
four well-known routing problems were used in this
experimentation. Additionally, in order to perform a fair and
rigorous comparison, two different statistical tests were
carried out: the Friedman's test and the Holm's test. Thanks
to this experimentation it can be affirmed that the proposed
method is competitive in terms of performance and
originality.
Design and implementation of a combinatorial
optimization multi-population meta-heuristic for
solving vehicle routing problems
Eneko Osaba and Fernando Díaz
Faculty of Engineering, University of Deusto, 48007, Bilbao, Spain
1
Title 2
Besides this, how the GB works with more complex
problems has been also shown in the thesis. These complex
problems have been directly extracted from real world
situations. For this purpose, different transportation problems
have been modeled and treated as complex routing problems.
In this aspect, the contribution is not only the application of
the GB to these problems. It should be added the formulation
and the treatment of them, because it is the first time that
such problems are addressed in the literature.
One of the most interesting real-world situations faced in
the thesis is related to the newspaper distribution [5]. More
specifically, the object of study was a medium-sized
newspaper distribution company. The area of coverage of this
company is at a provincial level, which means that it has to
serve a set of customers distributed in separate towns and
cities. The company has some principles, which are the base
of their logistic planning. The first principle is to treat towns
and cities as separate units. In this way, if one vehicle enters
a city, or a town, it was forced to serve each and every
customer located therein.
On the other hand, due to the current environmental
requirements, the company has a simple but robust paper
recycling policy. In this case, the objects to recycle are the
newspapers not sold the previous day. Thus, as can be
deduced, vehicles not only have to meet the delivery
demands of the customers. Besides that, they have to collect
at each point those newspapers that were not sold the day
before.
In addition, the company takes into account certain factors in
the routes planning process. The first one is related to the
hours at which the deliveries and collections are done. The
service is performed daily during morning from 6:00am to
15:00. Within this time window exists one range considered
as peak hours. In this way, traveling costs from one point to
another are greater if they are performed at peak hours.
REFERENCES
[1] E. Osaba. Design and implementation of a combinatorial optimization
multi-population meta-heuristic for solving vehicle routing problems.
PhD thesis, Deusto Institute of Technology University of Deusto,
November 2015.
[2] E. Osaba, F. Diaz, R. Carballedo, E. Onieva, and A. Perallos. Focusing
on the golden ball metaheuristic: An extended study on a wider set of
problems. The Scientific World Journal, 2014, 2014.
[3] E. Osaba, F. Diaz, and E. Onieva. Golden ball: a novel metaheuristic to
solve combinatorial optimization problems based on soccer concepts.
Applied Intelligence, 41(1):145–166, 2014.
[4] E. Osaba, X.-S. Yang, F. Diaz, P. Lopez-Garcia, and R. Carballedo. An
improved discrete bat algorithm for symmetric and asymmetric traveling
salesman problems. Engineering Applications of Artificial Intelligence,
48:59–71, 2016.
[5] E. Osaba, X.-S. Yang, F. Diaz, E. Onieva, A. D. Masegosa, and A.
Perallos. A discrete firefly algorithm to solve a rich vehicle routing
problem modelling a newspaper distribution system with recycling
policy. Soft Computing, pages 1–14, 2016.
Eneko Osaba received the University degree and
the Ph.D. degree in computer sciences from the
University of Deusto, Bilbao, Spain, in 2010 and
2015, respectively. His doctoral thesis is focused
on artificial intelligence, specifically in the field of
combinatorial optimization, studying and
developing heuristics and metaheuristics solving
routing problems. At the same time, in September
2014, he began his career as a Lecturer, teaching
statistics at the University of Deusto. He has participated in the development
of more than 45 papers, published in different international scientific journals
and conferences, being the first author in 29 of them. Among these papers, 21
of them have been published in international journals, having 15 of them JCR
Impact factor (including 9 Q1). According to Scopus, he is the most prolific
authors of the last 5 years under the search terms "Traveling Salesman
Problem". He served as a member of the program committee of international
conferences such as GECCO, HM and HAIS. Besides this, he is a regular
reviewer in several scientific journals such as Soft Computing, Information
Sciences, and Computers in Industry, and member of the editorial board of
International Journal of Artificial Intelligence.
Fernando Diaz holds a BA in Computer Science
and a PhD in Computer Science from the
University of Deusto, and he is currently a lecturer
of the Telecommunications Department at the
Faculty of Engineering of Deusto University.
From 1990 to 1992 he was an assistant researcher
in the Artificial Intelligence Department of
LABEIN research center (Bilbao, Spain). From
1992 to 1996 he was a PhD candidate in the
Computing Languages and Systems Department at
Deusto University, studying neural network
algorithms for combinatorial optimization. His
educational activity centers on the area of mathematics, teaching BA courses
on Industrial Engineering, Computer Engineering, and Telecommunications
Engineering. His main research interests include combinatorial optimization
meta-heuristics applied to production scheduling and logistics. Other research
interests include artificial neural networks and data mining. During the past
few years he has carried out diverse studies in the areas of intelligent
information retrieval, neural networks, genetic and evolving algorithms, and
production scheduling and distribution, participating in various research
projects funded by the European Commission, and the Basque Regional
Government. He is also part of the Industrial Management and Logistics
research team of the Engineering Faculty of Deusto University.
