Post on 04-Jul-2020
School of Computer Science
Automated Scheduling, Optimisation and Planning (ASAP) Research Group
Research Report 2007 - 2008
Welcome � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 3
The LANCS Initiative � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 4
Next Generation Decision Support � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 6
Timetabling � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 8
Sports Scheduling� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 9
Cutting, Packing and Space Management � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 10
Scheduling in the Air Transport Industry � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 12
Healthcare Modelling and Optimisation � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 14
Production Scheduling� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 16
Bioinformatics, Systems and Synthetic Biology � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 17
EPSRC IDEAS Factories � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 19
Outreach� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 20
Professional Activities� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 23
Publications � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 27
Grants � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 34
ASAP Personnel � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 38
PhD Students� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 41
Visiting Fellows/Associated Staff � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 43
How to find us � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 43
Contents
2 A S A P
Welcome to our 2007-2008 research report. The purpose of this
report is to highlight our activity and achievements over this
period. These two years have seen some notable achievements
by the group. In particular, we played a key role in the award of a
£13m programme across four universities (Lancaster, Nottingham,
Cardiff and Southampton) under EPSRC’s Science and Innovation
initiative. The consortium received over £5.4m in total from EPSRC
(Nottingham received £2m) and the institutions have contributed
over £7.5m. Nottingham’s contribution is over £2.4m and includes
the commitment of a new chair and two new lectureships.
In addition, our £2.6m EPSRC grant to explore the automation
of the heuristic design process has matured into an exciting and
groundbreaking initiative which is setting the international agenda
in automated methodologies for heuristic design. At the beginning
of 2009, we hold over £8m for our research activity from a wide
variety of funding bodies including EPSRC, BBSRC, Research
Council of Norway, KTP and industry.
Our strategic aim over the next few years is to continue to flexibly
respond to emerging research opportunities in order to build upon
our transformative research agenda and to extend our activity into
other disciplines and application areas. The cornerstone of our long-
term research strategy is to tackle the complexity and uncertainty
of real world problems and to innovatively address the broad
range of demanding scientific challenges which are generated by
industrial, commercial and service sector requirements. Our overall
strategic aim is to address a broad range of scientific challenges
at the interface of Operational Research and Computer Science
to underpin the exploration and development of computational
search methodologies that emerge from studying the complexity
and uncertainty of real world scheduling, optimisation and decision
support problems. Key strategic goals include:
Automating the Heuristic Design Process: The ASAP group has
set the international agenda in exploring the development of
computational methodologies that can automatically build decision
support systems. The aim is to investigate the extent to which
we can replace human decision making by computer algorithms
that are able to operate at the same level as humans. This is an
extremely challenging aim, which the scientific community is only
just beginning to explore. This has the potential to change the way
that decision support systems are built. It also has the potential to
significantly reduce the resource cost that is often associated with
developing bespoke systems.
Closing the gap between industrial/real world practice and
academic decision support research: We aim to explore dynamic
and complex computational modelling and intelligent decision
support techniques within the context of real world problems such as
transport scheduling (particularly in the airline industry), timetabling,
manufacturing technologies, bioinformatics, production scheduling,
protein folding, DNA mapping, healthcare personnel rostering,
radiotherapy planning and others. We aim to establish new decision
support methodologies that push the boundaries of automated
search methods and the complexity that they are able to handle.
Closing the gap between theoretical understanding and complex
decision support system development: ASAP has been extremely
successful in developing effective methodologies for complex
real world problem solving scenarios. However, the scientific
community’s level of theoretical understanding of what heuristics
work well in what situation is extremely low. Moreover, much of
the theoretical work in this area has dealt with models that are too
simple. We aim to develop a deeper theoretical understanding of
complex real world problem solving scenarios. This would facilitate
much more effective design of computational search methods than
is possible today.
Our strategic goals are undertaken within the context of a broad
inter-disciplinary agenda. Our core research on modelling and
search methodologies has redefined the inter-disciplinary interface
between Operational Research and Computer Science, while our
grounding in diverse applications involves dialogue with many other
disciplines. We aim to underpin an adventurous and ambitious
strategic programme with a clear multi-disciplinary focus that will
draw upon and influence a range of disciplines and new inter-
disciplinary application areas. Indeed, the potential benefits in
providing the grounding for tomorrow’s decision support systems
could be far reaching in laying the foundations for more efficient,
more effective, more environmentally friendly, cheaper, easier-to-
implement and easier-to-use decision support systems across many
industries and sectors.
This report provides a brief overview of the range and breadth of
our work over 2007-2008. We hope that you find it informative,
interesting and useful.
Welcome
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The LANCS Initiative is a collaboration of leading Operational
Research (OR) groups from four UK Universities. Specifically, it was
founded by:
LA Management School, Lancaster University
N School of Computer Science, University of Nottingham
C School of Mathematics, Cardiff University
S School of Mathematics and School of Management,
University of Southampton
Under the EPSRC Science and Innovation program it was awarded
the largest ever grant for OR within the UK – and possibly the largest
ever OR grant in the world. Together with co-funding from the
universities, it is an initiative project with the aim of building
OR theory in order to support OR practice. The Initiative officially
started up in September 2008 and is, therefore, still in the very
early stages, but it is expected to have a significant influence on the
development of OR within the UK, and, indeed, worldwide.
According to EPSRC’s 2004 International Review of OR, the UK
has a strong position in the practical application of OR. However,
in the wider community, there has generally been a ‘gap’ between
the theory and the practice. A primary goal of the LANCS
Initiative is to close this gap, and to contribute towards building
UK capacity in OR by setting the overall long-term national and
international research agenda.
The LANCS initiative is currently organised around six research
themes. Three of these are concerned with application areas that
are vital to the UK: Transport, Healthcare and Green Logistics.
The remaining three are concerned with important theoretical
research directions: Discrete Nonlinear Optimisation, Heuristic
Understanding and Systems to Build Systems, with the last two
being led by Nottingham. These two Nottingham-led themes are
explored in more detail on the opposite page.
The LANCS Initiative
ResearchThemes
GreenLogistics
HeuristicUnderstanding
Systems toBuild Systems
Transport
Healthcare
Discrete andNonlinear
Optimisation
4 A S A P
Many large problems are effectively and efficiently solved by the use of heuristics and meta-heuristics.
Such methods have no guarantee to return optimal solutions, but they often represent appropriate practical
methods to solve real world, large-scale problems. Typically, the methods have been built upon years of
experience. However, there is generally a lack of a deep understanding as to why and when they work. This
lack of understanding means that significant time-consuming trial and error experimentation is often required.
The aim of this research cluster is to strengthen the theoretical understanding of how heuristics work.
A particular aspect of this thematic programme is to better understand the landscapes of real search
spaces. The aim is to reduce the need for trial and error by developing our understanding so that
expensive mistakes (in terms of both time and resources) can be avoided and engineering efforts can
directed in more fruitful directions.
Currently, building an effective decision support system can often be a rather daunting task. There are
not many tools available, and so the experience of an expert plays a major role in the development of
systems and this is often characterised by trial and error. The net effect of this is that building systems is
expensive and so can often only be undertaken by large organisations that have the required resources
to devote to these projects. This often means that computational techniques are effectively unavailable
to small and medium sized enterprises. The aim of this theme is to develop the theoretical understanding
required to underpin the construction of tools and components to allow decision support systems to be
built automatically and adapted quickly. The idea is to effectively capture much of the role that normally
requires a human optimisation or computational search expert. This is a particularly challenging goal which
is being addressed in the almost complete absence of a mathematical and theoretical understanding of
how to build intelligent systems which are capable of automatically building new systems.
Heuristic Understanding
Systems to Build Systems
A Landscape Analysis of a search space.
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Next Generation Decision Support Automating the heuristic design process
The current state of the art in decision support and
search methodologies tends to focus on bespoke problem
specific systems. Such approaches could be made more
widely applicable, by automating the process of designing
problem solving techniques. Most real world decision
support systems represent vast search spaces of potential
solutions. These spaces are so large that it is usually
impossible to develop methods which can guarantee the
location of the optimal solution. In order to navigate such
enormous search spaces, the scientific community has, in
the last 30 years, increasingly turned towards the use of
heuristics and meta-heuristic methodologies (as well as
hybridisations with other approaches) to find high quality
solutions to such problems. Despite the success of meta-
heuristic methods in solving real-world computational
search problems by developing bespoke problem specific
methods, there are still some difficulties in easily applying
them to newly encountered problems, or even new
instances of known problems. These difficulties arise
mainly from the significant range of parameter or algorithm
choices when using these approaches, and the lack of
guidance on how to select them. Another drawback is that
these state-of-the-art approaches for real-world problems
tend to be expensive to develop and maintain.
The ASAP group has been awarded a £2.66m strategic
EPSRC grant which underpins our research efforts in
this area over a five year period (EP/D061571/1: Next
Generation Decision Support: Automating the Heuristic
Design Process). The potential benefits of success in such
a radical undertaking are enormous and permeate not
only the disciplines of Computer Science and Operational
Research but also the various disciplines that draw upon,
and contribute to them. These include Mathematics,
Business, Engineering, Computational Chemistry, Medicine,
Architecture (space management), Bioinformatics,
Manufacturing and all areas of Management. The
research also impacts upon automated heuristic selection
and design across many diverse applications such as
scheduling, timetabling, cutting/packing, protein folding,
catalyst optimisation, medical decision making and others.
This research initiative is investigating the development of
systems which are able to automate the heuristic design
process. A key principle behind this challenging research
goal is not only the automation of the underlying heuristic
selection process, but also the adaptation and evolution
of the higher-level algorithms, in order to respond to
changes in the problem specification or the environment.
In effect, we aim to replace the ‘knowledge engineer’ with
intelligent search based methods that can automatically
generate heuristic methodologies to suit the current
problem under consideration.
We are combining fundamental advances in the
underpinning areas of automated heuristic design, such as
Evolving 2D packing heuristics with Genetic Programming (by Matthew Hyde)
6 A S A P
Self-assembling hyper-heuristics for the travelling salesman problem (by German Terrazas)
machine learning, fuzzy systems, cooperative techniques,
and genetic programming with risky, exploratory research
into more adventurous areas such as software self-
assembly, in order to progress the automation of designing
heuristics. In this major undertaking, we are pulling
together, sometimes diverse strands, which we believe to
be complementary but which have often only been studied
in isolation. This is being undertaken in a wide range of
original investigations and novel hybridisations in order to
bring about a step change in decision support and search
methodologies – in short, the next generation of decision
support techniques.
In addition to the £2.66m grant, this major, strategic research
direction is supported by other EPSRC awards including:
Optimisation Methods in Health Care Planning • (Research Council of Norway):
£1m (£940K SINTEF and £60K Nottingham)
PLATFORM: Towards More General Optimisation/• Search Systems (EPSRC: GR/S70197/01):
£423K (Nottingham)
Genetic Programming in a Hyper-heuristic Framework • (EPSRC: EP/C523385/1):
£475K (£228K Nottingham, £247K Essex)
Hyper-heuristics for Scheduling, Rostering and Routing • (EPSRC: EP/C547377/1):
£32K (Nottingham and Montreal)
Moreover, the work is being theoretically underpinned
by the “Systems to Build Systems” theme of the LANCS
Initiative (see page 5).
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Timetabling is a difficult real world problem faced by many institutions all around the world.
ASAP maintains a leading role in timetabling research. The International Conference on the Practice and
Theory of Automated Timetabling (PATAT) is a biannual conference series that has been co-organised
by the ASAP research group since the series started in 1995. PATAT conferences provide a forum for
timetabling researchers and practitioners to share their ideas and experiences.
ASAP has also been leading the Association of European Operational Research Societies (EURO)
Working Group on Automated Timetabling (WATT) since 1996. Biannual workshops that complement the
PATAT conferences have been organised in alternating years within the EURO and IFORS conferences. A
digest of up-to-date timetabling news has been distributed at regular intervals to the WATT members.
A discussion list has been maintained for promoting timetabling research and providing a medium for
WATT members to exchange their ideas and opinions.
ASAP played a key role in organising the second International Timetabling Competition 2007, held
in collaboration with Queen’s University Belfast, Cardiff University, Edinburgh Napier University and
the University of Udine. The competition, consisting of three tracks covering examination and course
timetabling, received international attention and many submissions from across the world. The successful
approaches for each track were published in the PATAT2008 Proceedings.
In addition to our work on organising international meetings and events, the ASAP group investigates
the following topics at the cutting edge of timetabling research:
Compilation and modelling of new examination and course timetabling problems that capture real • world complexities
Development of intelligent computational search methodologies, such as, heuristics, meta-heuristics • and their hybrids for solving single and multi-objective real world timetabling problems, having a
rich set of features and constraints
Explanation of novel adaptive approaches, such as hyper-heuristics that can operate at a higher level • of generality than the current timetabling systems can support
Problem representation issues in timetabling• Investigation of the effect of different component choices for learning hyper-heuristics on their • performances and comparison of hyper-heuristics for timetabling and other problems
Integration of exact methods, such as integer programming and constraint programming with meta-• heuristics for solving large and highly constrained timetabling problems
The ASAP group’s world-leading into automated timetabling underpins the commercial activity of one
of its spinout companies EventMap Ltd (see page 22).
Timetabling
8 A S A P
Many sporting events are structured as a round robin tournament
(where every team plays every other team), or as a double round
robin tournament (where every team plays every other team twice;
once at their home venue and once at the other team’s venue).
Other events are based on a knockout structure where a team is
eliminated from the competition once they have lost. Some events,
such as the football World Cup, combine elements of a round robin
tournament, followed by a knockout stage.
Although the algorithm to produce a (double) round robin
tournament is well known, the generation of a feasible fixture list is
often complicated by the many additional factors that have to be
taken into account.
Within the research group, we have focussed on the double round
robin tournament for the English football league. In particular, we
have addressed a subset of the problem which aims to minimise the
travel time for supporters over the Christmas/New Year period.
The fixtures that are played at this time of the year have been
constructed with the goal of having the minimal travel time when
compared against other dates in the calendar. We have demonstrated
that we are able to further reduce the travel distances when compared
to the fixtures that are published by the football authorities.
We are currently extending this work in a number of ways.
For example:
Improving the search methodology that is utilised.• Investigating the multi-objective aspects of the problem. • That is, still trying to minimise the distance travelled by the
supporters but also reduce policing costs by limiting the number
of local derbies.
Scheduling a sequence of four matches over the Christmas • period, while still minimising the travel distances. This is
important during World Cup years when efforts are made to
shorten the season.
There are many other sports scheduling problems, such as • referee assignment, which do not fall into the category of round
robin or knockout tournaments, but which represent complex
scientific challenges.
Sports Scheduling
Notts County Football Ground: The oldest football club in the world (Graham Kendall)
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Cutting and packing problems impact upon a broad range of
industries where the efficient usage of space or material is an
important consideration within the manufacturing life cycle. For
industries such as textiles, automobile production and aerospace
(amongst others), even a small reduction in the material required to
produce each product or component can translate into significant
cumulative cost savings along with a real environmental impact in
terms of more efficient use of resources.
ASAP has carried out leading edge research into two-dimensional
cutting and packing problems for well over a decade and has
published some of the best performing heuristic and meta-heuristic
algorithms within the literature for both rectangular and irregular
variants of the problem. More recently, ASAP has added three-
dimensional problems to its research portfolio and continues to
publish novel approaches that are producing some of the best-
known solutions on benchmark instances.
Due to the success of ASAP’s research into cutting and packing
problems, Aptia Solutions Ltd (a spin-out company) was
formed to further develop these approaches and explore other
commercialisation opportunities. This has lead to the development
of a number of new products which are increasingly being adopted
by a wide variety of industries.
Many of the research challenges, when developing cutting and
packing algorithms, arise due to the underlying geometry that has
to be dealt with. Our work on the No Fit Polygon has provided other
researchers with algorithms to utilise this important data structure. We
have also reported the benefits of using an efficient implementation
of the No Fit Polygon in some of our other publications.
We have held a variety of awards which have funded our work in
cutting and packing. These include PLATFORM: Towards More
General Optimisation/Search Systems (GR/S70197/01), Next
Cutting, Packing and Space Management
One of the products developed by our spin-out company, Aptia Solutions Ltd: Simulation of a cutting machine
10 A S A P
Generation Decision Support: Automating the Heuristics Design
Process (EP/D061571/2) and An Investigation of Cutting/
Packing and Planning using Automated Algorithm Selection (GR/
S52414/01).
Space allocation is concerned with making the most effective use
of the space available. This might be office space, university lecture
halls, academic offices etc. Our work in this area is underpinned by
an EPSRC award entitled Adaptive Multi-objective Heuristic and
Meta-heuristic Approaches to Space Planning (GR/T26115/01).
This project largely focussed on university teaching space allocation.
Within educational institutions, teaching space is an expensive
resource to provide and maintain. Providing too much space will be
costly, providing too little space or an inappropriate mix of room
sizes and facilities could negatively impact the satisfaction amongst
both staff and students. Our work on space allocation has provided
new methods to handle this delicate balance. It has been based
on novel uses of the theory of thresholds and phase transitions
to explain how the properties of systems can change rapidly with
seemingly mild changes in their characteristics.
Our work in cutting, packing and space allocation has benefited
from an EPSRC NETWORK award called NETWORK: Interdisciplinary
Cutting, Packing and Space Allocation (EP/D031079/1) which,
amongst many other activities enabled us to organise a Dagstuhl
seminar on Cutting, Packing and Space Layout (Seminar 07112),
which led to selected papers being published in a special issue of
Annals of Operations Research.
Aptia Nest
“ Our work in this area is
underpinned by an EPSRC
award titled as Adaptive
Multi-objective Heuristic and
Meta-heuristic Approaches to
Space Planning.”
London Heathrow is one of the busiest airports in the world,
having more international passengers than any other airport. The
sequencing of departures at the runway is an important but complex
problem which directly affects the delay for outbound flights.
However, this problem is currently solved manually by a runway
controller in the control tower at Heathrow. Although the controllers
perform excellently (simulations indicate that delays would be over
four times as long without the re-sequencing performed by the
controllers), with up to one take-off a minute, there is insufficient
time for controllers to consider all possible good take-off sequences.
Funded by EPSRC and NATS (formerly National Air traffic Services)
Ltd, through the Smith Institute for Industrial Mathematics, the
ASAP group has investigated the feasibility of harnessing the
power and flexibility of modern computational search techniques
to provide accurate advice to controllers via an intelligent decision
support system. The aim is to provide the controllers with the
information which would be necessary to enable them to reduce
delay even further. This involves considering not only the value of
a take-off sequence, but also whether it can be attained and how
hard the manoeuvring on the ground would be to attain it. The
challenge for developing such a decision support system is not only
to provide accurate advice, but also to do so quickly enough so
that the controllers are able to respond immediately to situational
changes at this popular and busy airport. The algorithms which
have been developed are fast enough to find lower delay take-off
sequences in less than a second.
Reducing delay at the runway is good for passengers, airlines and
the environment, but the throughput of the runway is limited.
Queues can, therefore, sometimes accumulate no matter how good
the scheduling is. To further reduce the environmental impact at
the airport, another ASAP project, again funded by NATS and
EPSRC, considers the advantages of holding aircraft on the stands,
before their engines are started, rather than at the runway. This
decision support system considers all of the aircraft at the airport,
predicts the likely take-off times (and the consequent delay), then
absorbs as much delay as possible at the stand by delaying the
pushback time from the stand, where necessary. The difficulty here
is that, not only do the take-off sequence restrictions have to be
considered, but so do issues of contention between aircraft which
are at stands which are close together. It is impossible for aircraft
to simultaneously push back from some combinations of stands,
and there are other combinations where one aircraft would be
blocked by another and would have to wait for it to move out of
the way before it could start its own pushback. Simulations using
the developed system have shown considerable decreases in engine
Scheduling in the Air Transport Industry
12 A S A P
running time, thus, NATS are planning to integrate the system
into Heathrow soon. In both of these cases, the problem under
consideration is too large to solve exactly within the permitted
time-frame, but our innovative modelling and heuristic techniques
make it possible to produce algorithms to find high quality solutions
quickly enough for practical use.
The ASAP research group has also worked in close collaboration
with Air-France-KLM to investigate new approaches to build
more robust schedules. The approach implements minor changes
in existing schedules which help to improve robustness and
reduce passenger delays. A more robust schedule is one that
is less sensitive to disruptions on the day of operation, offers
increased flexibility to recover from disruptions, and prevents
delay propagation through increased schedule stability. The
approach developed by ASAP facilitates the investigation of
mutual interaction between multiple robustness characteristics of
a schedule and the quantification of their simultaneous influence
on the schedule’s operational performance. This research has
contributed to fundamental new insights in the robustness of airline
schedules, and a better understanding thereof. This underpins the
development of future models for robust airline scheduling. The
fundamental contribution of this research project was internationally
recognised: it was awarded the best technical innovation at the
Airline Operations Meeting of the Airline Group of the International
Federation of Operational Research Societies, Denver, Colorado,
2007 and it received second place in the Anna Valicek Competition
for innovative research in Airline Research (see http://www.agifors.
org/award_home.jsp).
The modern air transportation system is a complex environment,
where many different optimisation and search problems have to be
solved every day. Further currently planned research (supported
by EPSRC, NATS and both Manchester and Zurich airports) will
cover the integration of the arrival sequencing problem, the stand/
gate allocation problem and modelling and optimisation of other
ground processes at Manchester, Zurich and other airports. Again,
the emphasis is to consider the real problems that are faced at
real airports, in order to obtain real benefits for airports, airlines,
passengers and the environment.
The number of cancer cases in the United Kingdom has continually
increased in the last few decades. Every year, 200,000 people are
diagnosed with cancer in England and 120,000 people lose their
lives to the disease. Efficient radiotherapy planning and resource
management in oncology departments has been recognised as a key
factor to their smooth running. The various activities, starting from
patient referral through to the delivery of the appropriate treatment,
form a complex system, for which generating a high quality planning
and scheduling solution is a challenging real-world problem that has
significant impact on healthcare staff and patients.
We are carrying out an ambitious research program on radiothearpy
planning and scheduling supported by EPSRC (EP/C549511/1:
Novel Approaches to Radiotherapy Planning and Scheduling in the
NHS). This is a joint research project with Coventry University.
Two large hospitals, Nottingham University Hospitals NHS Trust and
the University Hospitals Coventry and Warwickshire NHS Trust, are
acting as collaborators on the project. Both of them are providing
radiotherapy treatment to a large population throughout their
respective regions.
This research requires a multidisciplinary effort, aiming to
combine different Operational Research and Artificial Intelligence
methodologies within a complex real-world medical environment.
The main objectives of the project are:
1. To develop a prototype intelligent decision support system
to assist oncologists and medical physicist, in the generation of
radiotherapy treatment plans for patients. In a substantial part of
the published literature on radiotherapy, treatment planning has
been set as an optimisation problem where the objectives are to
achieve a uniform tumoricidal dose and to minimise the radiation
dose received by organs at risk and by healthy surrounding tissue.
However, all of the proposed approaches start from scratch in
creating a plan for each patient, rather than understanding a new
problem in terms of old cases. Our aim is to explore new directions
Healthcare Modelling and Optimisation
A plan for a radiotherapy treatment for brain tumor
14 A S A P
in radiotherapy planning, which use the knowledge and experience
gained in solving previous problems to address treatment planning
for new patients. So far, we have developed a case-based reasoning
system for prostate cancer planning which aids in determining the
dose plan for a new patient, by using the knowledge and experience
of an oncologist gained in treating previous patients. Also, we are
working with medical physicists to develop a system which will assist
in the planning of brain tumour treatments.
2. To develop a prototype decision support system to assist
in scheduling resources in radiotherapy. In this area, patient
scheduling has been recognised as a key factor for increasing the
quality of treatments, as it is of paramount importance to deliver
the treatment by the imposed waiting time target and to enable
consecutive treatment sessions without interruptions. Before
starting a radiotherapy treatment, a patient needs to go through
several phases, including the localisation of treatment fields using
a CT scanner or simulator, radiotherapy planning in which the
dosage and the best way to deliver radiation is determined and
the verification of a plan using a simulator. We develop systems for
both the scheduling of pre-radiotherapy phases and the scheduling
of patient sessions on linacs (linear accelerators). Our research on
radiotherapy scheduling was awarded a Certificate of Merit at the
International Conference on Intelligent Automation and Robotics,
within the World Congress on Engineering and Computer Science
(WCECS’08) in 2008.
Our system for radiotherapy scheduling provides the user with a friendly
windows-based interface
Another key area for the research group is in the development of
staff rostering methodologies and systems for health care. Some of
the most complex and challenging duty allocation problems arise in
hospitals and healthcare centres, for example nurse and physician
rostering. The benefits of automating the rostering process in these
situations include reducing the planner’s workload and associated
costs and being able to create higher quality and more flexible
schedules. This has become more important recently in order to
retain nurses and attract more people into the profession. Better
quality rosters also reduce fatigue and stress due to overwork and
poor scheduling and help to maximise the use of leisure time by
satisfying more requests. A more contented workforce leads to
higher productivity, increased quality of patient service and a better
level of healthcare. The ASAP group has developed a number of
novel and successful rostering algorithms for real world scenarios.
These approaches include tabu search, variable neighbourhood
search, memetic algorithms, case-based reasoning, hyper-heuristics,
hybrid methods and many others. In order to scientifically test
and validate these algorithms, we have created a suite of highly
challenging benchmark data sets from a number of hospitals around
the world. These data sets are publicly available at
http://www.cs.nott.ac.uk/~tec/NRP/.
In developing new algorithmic approaches, we often work closely
with industrial and non-academic partners. For example, in one of
our ongoing projects we are collaborating with SINTEF (the largest
independent research organisation in Scandinavia) on producing
more robust algorithms and flexible models for nurse rostering
through the application of hyper-heuristics.
Roster Booster – a modelling tool, solver and solution viewer for nurse rostering
15 www�asap�cs�nott�ac�uk
In most real-life modern environments, scheduling is an ongoing
process where circumstances in both external business environments
and in internal production environments may dynamically change. In
order to ensure high customer service level and profitability, there is a
need to respond to disturbances such as machine breakdowns, delays
in the arrival of materials, the arrival of rush orders and changes in
orders. The goal then is to modify the existing schedules, either to
restore their feasibility or to produce a higher quality schedule.
We are continuing our collaboration with Sherwood Press, a printing
company based in Nottingham. This collaboration started with
an EPSRC funded project (GR/R95319/01: Fuzzy Multicriteria
Approaches to Scheduling and Rescheduling Problems in Uncertain
Environments) which investigates the problem of integrating
new orders into the current schedule. The aim is to obtain a new
schedule with a good performance which is at the same time
stable, i.e. it resembles the initial schedule as closely as possible. In
particular, we have developed matchup algorithms, which define a
rescheduling horizon within which to schedule operations required
by a new order, enabling the remaining part of the initial schedule
to stay unchanged. Our research was awarded the best paper award
at the 21st International Conference on Industrial, Engineering &
Other Applications of Applied Intelligent Systems (IEA\AIE) in 2008.
Production Scheduling
Sherwood PressOver the last decade, fully automated manufacturing systems have
enormously increased the flexibility and efficiency of the production
process. In these flexible manufacturing systems, the material
(jobs) can be moved between machines automatically with the help
of transport robots (guided vehicles). In addition to the regular
problem of job shop scheduling, the transport of jobs between the
machines has to be planned ahead. Our aim, in another challenging
research direction, is to find efficient methods to solve the problem
of cyclic scheduling and transportation as an integrated operation.
Our prototype system for scheduling provides a user-friendly windows-based interface
16 A S A P
Data mining for BioinformaticsWe are carrying out an innovative and ambitious research
programme at the interface of data mining and bioinformatics.
Recent advances have demonstrated the potential for success in
developing ever more general, intelligent and reliable classification
and predictive technologies. We are exploring the feasibility of
designing and implementing robust classification and predictive
systems that can deliver high quality bioinformatics predictions
while making these predictions human-understandable. We
have built Array Mining, a server for automatic analysis of DNA-
microarray data. It provides a combination of three web interfaces
dedicated to three major tasks in gene expression analysis: Feature
selection (Gene Selection), Clustering (Class Discovery) and
Prediction (Class Assignment). Array Mining performs automatic
parameter selection, generates tabular and visual outputs (e.g. 2D-
and 3D-VRML plots) as well as links to public online data bases to
make functional gene annotations available with only a few mouse
clicks. We have also produced the following cutting edge learning
classifier systems that are capable of dealing with extremely large,
noisy and ambiguous bioinformatics data-streams.
PSP-Server, predicts geometrical features in proteins’ • native 3D structure
ArrayMining, provides a sophisticated algorithmic pipeline for • analysing DNA microarray data.
The ASAP research group is exploiting exciting Multi-disciplinary
synergies with the School of Chemistry, the Center of Bio-molecular
Sciences, the School of Pharmacy and the School of Life Sciences.
All these collaborations are performed under the umbrella of the
Inter-disciplinary Optimisation Laboratory. Our activities cover the
three themes of Structural Bioinformatics, Data mining, Systems
Biology and the emerging field of Synthetic Biology. Our software is
publicly available at www.infobiotic.net.
Structural BioinformaticsFunded through various ESF, BBSRC and EPSRC grants (e.g.
ESF-2482, EP/E017215/1, GR/T07534/01, BB/C511764/1), the
ASAP group carries out research at the cutting edge of structural
bioinformatics. We have developed ProCKSI, a decision support
system for protein structure comparison that computes structural
similarities using a variety of measures to produce a consensus. It
contains tools for visualising, analysing and easily comparing all
results, linking to external resources for further information and
literature about protein structures. These services are part of a
greater investigation of a suitable framework/architecture for very
large scale protein structure comparison, clustering and analysis
in parallel and distributed environments, involving the evaluation
and selection of, optimal middleware software, database model,
programming libraries, tools and algorithms.
We have also developed a Protein Structure Feature Prediction
server containing a collection of web services that use learning
classifier systems to predict Protein Structure Prediction (PSP)
sub-problems such as coordination number, solvent accessibility or
recursive convex hull. These subproblems are structural features of
protein residues that contain information about the end product
of the folding process. These features are related to the density of
packing of different parts of a protein or how buried/exposed,
far/close to the surface are different residues within a protein.
Bioinformatics, Systems and Synthetic Biology
17 www�asap�co�uk
Systems Biology and Synthetic BiologySystems Biology seeks to achieve an integrative, quantitative
understanding of biological systems spanning multiple length,
time and organisational scales from the subcellular, to the cellular,
colony/tissue scales all the way to ecological levels. On the other
hand, Synthetic Biology (an emerging discipline) seeks to produce
robust and reliable computational/mathematical techniques to
calculate or compute, based on a set of phenotype requirements,
the experimental route to their wet lab implementation. We are
developing an in-house methodology, InfoBiotics, that could
deal with the multiple challenges facing Systems and Synthetic
Biologists. Infobiotics, funded from multiple sources (e.g., EP/
E017215/1, BB/D0196131) is the synergy of executable biology,
evolutionary and machine learning methods, mesoscopic simulation
techniques and experimental data for a more principled practice
of origins of life, bioinformatics, computational systems and
synthetic biology research. P systems, computing with membranes,
abstract the structure and function of the living cell into a
formalism upon which we are building a multi-scale modelling
environment. By applying discrete, numerical simulation algorithms
to P system models of quorum sensing in the bacterial pathogen
Pseudomonas aeruginosa and root development in Arabidopsis
thaliana, we aim to understand the stochastic processes governing
these model organisms in order to guide laboratory experiments.
In addition, we use evolutionary search and machine learning
(Genetic Programming, Learning Classifier Systems, Support Vector
Machines, etc) to estimate parameters and discover structures that
match observed behaviour in cellular networks with the intention of
isolating these modules for use in the design of synthetic organisms.
Dissipative Particle Dynamics underpins our simulation of (proto-)
membranes. These activities are complemented by other groups
in the SynBioNT Synthetic Biology Network for Modelling and
Programming Cell-Chell Interactions (BB/F01855X/1) and The
CHELL: A Bottom-Up approach to in vitro and in silico Minimal Life-
like Constructs (EP/G042462/1).
The InfoBiotics pipeline includes complex combinatorial, continuous
and mixed discrete/continuous optimisation of systems/synthetic
biology models as well as a state-of-the-art simulator, model
checking capabilities and cutting-edge user interface.
The InfoBiotics pipeline
18 A S A P
EP/D021847/1: Chemical Craftwork: Evolvable CHELLware
Principal Investigator: Natalio Krasnogor
In a wide range of fields, computer algorithms which mimic an
evolutionary (or Darwinian) strategy are finding many applications,
particularly in the field of evolvable hardware where field
programmable gate arrays can be ‘constructed’ by evolutionary
programs to perform complicated tasks. This project is based on
the analogous idea of using evolutionary algorithms to develop
chemical functionality in arrays of ‘programmable’ chemical
reactors, based on current lab-on-a-chip and electrochemical cells.
In the future, we hope to apply these methods to complex reactors
built in artificial vesicles which communicate by chemical signals
much like a living cell. This project is closely linked with a network
grant (EP/D023343/1; CHELLnet: A Unifying Investigation in
Artificial Cellularity and Complexity) which was funded from the
same IDEAS factory.
EPSRC IDEAS FactoriesASAP researchers have been successful participants of a number of
EPSRC IDEAS Factories, which are based around a ‘sandpit’ event
where researchers from a variety of institutions and disciplines
meet to discuss innovative research ideas around a common theme.
ASAP’s interactions with these events have led to the following
funded projects.
EP/E017975/1: An Investigation of
Regulatory Decision Making by Automated Decision Makers
Principal Investigator: Graham Kendall
This project comprises a multi-disciplinary, inter-institutional team
drawn from Computer Science (Nottingham), Psychology (London
School of Economics) and Industrial and Manufacturing Science
(Cranfield). A two-year research assistant (based in Nottingham) is
supported by a PhD student based at Cranfield.
The project is modelling regulatory decision making environments,
including salt intake, nuclear waste disposal and avian flu and it is
studying how personality and power influences the decision
making process. Interviews with the decision makers have provided
input to a computer simulation which is exploring whether or not
we are able to mimic the decisions that were actually made. The
simulation environment draws heavily from the psychology literature
in modelling personalities and the power structures that exist in
group decision making.
EP/H004424/1: Integrating and Automating Airport Operations
Principal Investigator: Edmund K� Burke
This project represents a wide ranging multi-disciplinary and
cross-institutional initiative to exploit recent research advances
in automated search methodologies and decision support
techniques for airport operations. The proposed programme of
research will build integrated computational models of four key
airport operations: take-off scheduling, landing scheduling, gate
assignment and baggage flow. The project will explore how to
build computational models that represent the integration of
these problems and it will explore how to develop effective
multi-objective search methodologies which will employ those
models. Integrating these four operations and exploring new
and exciting ways of generating high quality solutions to the
integrated problem is the broad basic aim of the proposal. We
will work closely with colleagues at Manchester and Zurich
airports to ensure that we have continuous access to real world
expertise and data.
19 www�asap�cs�nott�ac�uk
The ASAP group carries out interdisciplinary research in collaboration
with researchers and practitioners from a wide range of different
backgrounds. Our existing research themes cover Computer Science,
Artificial Intelligence, Operational Research, Biology, Chemistry,
Psychology and many more. We work, and publish, with scientists
from many different countries including Australia, Belgium, Canada,
China, France, Germany, Iceland, Italy, Malaysia, Norway, Poland,
Spain, The Netherlands, Turkey and USA. We maintain fruitful
partnerships with the leading scientists in the UK and beyond,
through scientific networks and visiting fellowships. In addition to
the visiting fellowship grants held by the group, many self-funded
leading researchers from all over the world visit the group to engage
with the high quality research carried out within ASAP.
Members of the ASAP group have key editorial roles in many
of the leading journals in our areas, including Evolutionary
Computation, IEEE Transactions on Computational Intelligence and
Artificial Intelligence in Games, IEEE Transactions on Evolutionary
Computation, INFORMS Journal on Computing, Journal of
Heuristics, Journal of Scheduling and Memetic Computing. Many
members of the group are also represented on the program
committees of the leading conferences in our areas.
ASAP has established two international conferences:
MISTA (Multidisciplinary International Conference on • Scheduling: Theory and Applications) has been run in 2003,
2005, 2007 and will be run in Dublin in 2009.
PATAT (Practice and Theory of Automated Timetabling) has • been run in 1995, 1997, 2000, 2002, 2004, 2006, 2008. The
2010 conference will take place in Belfast.
The strength of our outreach capacity is further reflected by the
continuous success of the group in transferring and commercialising our
research findings. The group has three spinout companies (Event MAP
Ltd, Aptia Solutions Ltd and INFOHUB Ltd). It also collaborates with
a number of other industrial partners including Air France, BT Group,
Denby Pottery Company Ltd, DIPS ASA, GAT-Soft AS, Gower Optimal
Algorithms Ltd, KLM, Merlin Systems Corp Ltd, NATS, Nottingham City
Hospital, Ortec, Realtime Solutions Ltd, Sherwood Press and Walsgrave
General Hospital. Moreover, the group holds two recently awarded
Knowledge Transfer Partnership grants (with Midland Software Ltd
and 3T Logistics Ltd). ASAP also covers postgraduate research training
and public communication by involvement in initiatives aimed to
improve the provision of high quality taught courses for PhD students.
Moreover, the group is committed to raaising public awareness about
our scientific research and its impact on industry and businesses.
Visiting FellowshipsASAP continues to collaborate with many leading scientists from
around the world. These collaborations have been underpinned by
EPSRC Visiting Fellowships that the group has held since 2002. This
has enabled regular visits by:
Moshe Dror (GR/S07124/01) 2002-2003• Jacek Blazewicz (GR/S64530/01) 2003-2007• Amon Meisels (GR/S53459/01) 2003-2004• Michel Gendreau (EP/D027039/1) 2006-2011• Peter Brucker (GR/T23374/01) 2004-2009•
NATCORThe ASAP group plays a leading role in the NATCOR (A National
Taught Course Centre in Operational Research) initiative which
aims to improve the provision of high quality taught courses in
Operational Research for PhD students. NATCOR is funded by
EPSRC for the first five years of its life (October 2006 to September
2011) and is supported by the Operational Research Society which
is a collaborating partner.
Outreach
ASAP is collaborating with Prof
Peter Brucker through an EPSRC
Visiting Fellowship award and other
collaborative grants.
Prof David Goldberg giving a seminar
during his ASAP visit
20 A S A P
Internationalisation ASAP now has representation at both of our international campuses
in Malaysia and China. Ruibin Bai (China), Andrzej Bargiela
(Malaysia) and Siang Yew Chong (Malaysia) play an active role in
the research group, supported by close collaboration with ASAP
personnel at all three campuses. The MNDP (Malaysia Nottingham
Doctoral Program) also supports exchanges between ASAP and
colleagues in Malaysia. A recent Chinese government award has
enabled this level of research activity to be further increased.
Knowledge Transfer PartnershipsThe group has recently been awarded two Knowledge Transfer
Partnership (KTP) grants to further develop and apply our search
and optimisation technology for the benefit of companies in our
region. The KTP programme (http://www.ktponline.org.uk/) is a
high profile government initiative to fund the transfer of knowledge,
technology and skills to SME’s to help them become more
competitive and efficient. Our two projects can be outline as follows:
KTP 7074 is a partnership with Midland Software Limited • (http://www.midlandhr.com/), a company that provides a
range of HR Management solutions, to develop a personnel
scheduling engine.
KTP 7449 is a partnership with 3T Logistics Limited, a company • that provides outsourced transport solutions, to develop an
automated carrier management and scheduling system.
Both of the above projects will have duration of two-years and
are tangible evidence of our capacity to transfer the expertise and
technology developed within the ASAP group to the real-world.
The Interdisciplinary Optimisation Laboratory (IOL) The IOL is a multi-disciplinary research institute at the University
of Nottingham. The lab is based on the Jubilee Campus, in the
School of Computer Science and is closely linked to the Automated
Scheduling, Optimisation and Planning Research group (ASAP).
The IOL carries out inter-disciplinary research across the fields of
Computer Science, Biology, Chemistry, Physics, Management Science,
Mathematical Sciences, Medical Sciences, Psychology and Robotics.
In particular, it focuses on developing innovative and competitive
search methodologies and intelligent decision support systems with
an emphasis on transdisciplinary computational search, the modelling
of complex systems and very-large datasets processing.
Some of the IOL current projects include collaborations with Prof.
P. Moriarty and Prof. P. Beaton from the School of Physics and
Astronomy on the optimization of scanning probe microscopy
procedures. With Prof. N. Champness and Prof. J. Hirst in the
School of Chemistry, the IOL investigates optimisation strategies
for designing molecular tiles and protein structures respectively.
The IOL also carries out research at the cutting edge of systems and
synthetic biology in collaboration with Prof. M. Camara and Prof.
P. Williams at the Centre for Biomolecular Sciences, with Prof. C.
Alexander in the School of Pharmacy, Prof. C. Hogman and Prof.
M. Bennett in the School of Biosciences. With Dr. N. Russell at the
Schools of Biology and Electrical and Electronical Engineering, IOL
researchers are developing unconventional computing paradigms
based on photo-switching molecular constructs.
21 www�asap�co�uk
Spin Out Companies and Knowledge Transfer PartnershipsEventMAP (see www.eventmap-uk.com) is a joint venture
between the University of Nottingham and Queen’s University
Belfast. An innovative example of fusion between research and
practice, this spin out company specialises in Institutional Strategic
Resource Planning. The company provides business analysis,
methodology and software product to allow for more effective
usage of organisational resource. The company currently deploys
the state-of-the-art Optime Scheduling Engine within their unique
‘Optimisation in Practice’ methodology. Currently, solutions are
available for the purposes of timetabling and resource planning
software solutions for the challenging problems faced by today’s
educational institutions in the areas of lecture and room scheduling,
noncurricular events scheduling and examinations scheduling. At
the heart of EventMAP’s solution provision is the integration of
leading edge research based scheduling techniques which have
the capability of providing significant institutional cost savings
through more efficient and more satisfactory use of resource.
Through the development of an innovative partnering business
model, Staff at EventMAP have implemented solutions in Europe,
New Zealand, Australia, America, Middle East, Asia and China. The
Company has also taken a leading role in helping to organise the
second International Timetabling Competition and the PATAT2010
conference in Belfast.
Aptia Solutions (see www.aptiasolutions.com) is a software
development company that has grown out of the research
developed by ASAP. It specialises in the development of powerful
yet easy to use automated nesting products for industries in which
CNC machining is an important part of the manufacturing lifecycle.
Aptia spun-out from the University of Nottingham in 2004 and
started selling the first of its commercial products in January 2008.
At the time of writing, over 80 customers rely on Aptia’s products to
support their cutting requirements and maximise material utilisation.
The powerful cutting and packing algorithms developed by ASAP
researchers underpin Aptia’s products.
INFOHUB (see www.infohub-ltd.co.uk) is a research-led
company developing system solutions for information acquisition,
processing and delivery. The company specialises in human-
centred applications such as decision support, data mining and
information services in traffic, transport and environmental sectors.
The systems developed at InfoHub enhance the value of both the
existing information sources and current and future communications
technology. The spectrum of systems ranges from information
systems for the general public through to specialised information
management tools that offer several levels of information
generalisation required to support decision-making processes in
complex systems and in the presence of uncertainty.
“ The group has recently been awarded two Knowledge
Transfer Partnership (KTP) grants to further develop and
apply our search and optimisation technology for the
benefit of companies in our region.”
22 A S A P
Professional ActivitiesEPSRC Peer Review CollegeA. Bargiela, E.K. Burke, G. Kendall, N. Krasnogor, J.D. Landa-Silva, S. Petrovic
Company DirectorshipsA. Bargiela:
Director of INFOHUB Ltd.• E.K. Burke:
Research Director of EventMAP Ltd.• Director of Aptia Solutions Ltd.•
G. Kendall:
Director of EventMAP Ltd.• Director of Aptia Solutions Ltd.•
FellowshipsE.K. Burke:
Fellow of the British Computer Society• Fellow of the Operational Research Society•
G. Kendall:
Fellow of the Operational Research Society•
National and International Committee MembershipsA. Bargiela:
Management Board of the European Council for Modelling and Simulation (immediate past Chairman)• Visiting Professor, University of Alberta, Canada• Special visiting fellow, Konan University, Japan•
E.K. Burke:
Jury for the (EURO) Gold Medal, 2007 • EPSRC Mathematics Strategic Advisory Team for Mathematics, 2008 • Advisory Board of the EPSRC IDEAS Factory Network on Productivity• Advisory Board of the EPSRC SEBASE (Software Engineering By Automated SEarch) Initiative • Executive Committee of the EPSRC National Training Centre for Operational Research • International Advisory Board of the BBSRC/EPSRC Centre for Plant Integrative Biology • Scientific Steering Committee of the Isaac Newton Institute for Mathematical Sciences (nominated by EPSRC) • Scientific Committee of the Smith Institute for Industrial Mathematics and System Engineering • European Science Foundation (ESF) Pool of Reviewers • UK Computing Research Committee (UKCRC) • General Council of the OR Society • Education and Research Committee of the OR Society • Management Board of the EPSRC LANCS Initiative • Executive Committee of the EPSRC LANCS Initiative • Scientific Board of the Centre of Excellence for Research in Computational Intelligence and Applications (CERCIA), 2007 (Chairman)•
23 www�asap�cs�nott�ac�uk
G. Kendall:
Dagstuhl Seminar on Cutting, Packing and Layout, 2007 (Co-chair)•
E. Özcan:
Executive Committee of the EPSRC LANCS Initiative•
A. Parkes:
Executive Committee of the EPSRC LANCS Initiative•
S. Petrovic:
EURO (European Association of Operational Research Societies) Working group on Automated Timetabling (WATT) (Coordinator)•
Journal EditorshipsJ. Bacardit:
Guest Co-editor of a forthcoming special issue of the Memetic Computing journal on ‘Metaheuristics for Large-Scale Data Mining’.•
A. Bargiela:
Associate Editor of IEEE Transactions on Systems, Man and Cybernetics, Part A• Editor-in-chief of Modelling and Simulation in Engineering, Hindawi Press• Editorial Board of Information Sciences (Member)• Editorial Board of International Journal of Intelligent Decision Technologies (Member)• Editorial Board of Journal of Advanced Computational Intelligence and Intelligent Informatics (Member)• Editorial Board of International Journal of Knowledge Engineering Systems (Member)• Editorial Board of International Journal of Simulation: Systems, Science & Technology (Member)•
E.K. Burke:
Editor-in-chief of the Journal of Scheduling • Area Editor (for Combinatorial Optimisation) of the Journal of Heuristics published by Springer • Associate Editor of the INFORMS Journal on Computing • Associate Editor of the IEEE Transactions on Evolutionary Computation • Editorial Board of Memetic Computing (Member)• Guest Co-editor of a feature issue of the European Journal of Operational Research (EJOR ) • on ‘Evolutionary and Metaheuristic Scheduling’, 2007
G. Kendall:
Associate Editor of the Journal of the Operational Research Society• Editorial Board of IEEE Transactions on Evolutionary Computation (Member)• Editorial Board of IEEE Transactions on Computational Intelligence and Artificial Intelligence in Games (Member)• Editorial Board of Computational Intelligence (Member)• Editorial Board of the International Journal of Systems Science (Member)• Editorial Board of Intelligent Systems in Accounting Finance and Management (Member)• Associate Editor of INFOR: Information Systems and Operational Research• Editorial Board of Cognitive Neurodynamics (Member)• Editorial Board of International Journal of Intelligent Computing and Cybernetics (Member)•
24 A S A P
N. Krasnogor:
Founding Editor-in-chief (technical) for the Memetic Computing Journal, Springer• Associate Editor for Evolutionary Computation, MIT• Editor for Modelling and Simulation in Engineering, Hindawi Publishing Corporation• Editor for Journal of Artificial Evolution and Applications (Hindawi Publishing Corporation• Guest Editor for Natural Computation, Springer, special issue on Nature Inspired Cooperative Strategies for Optimisation, 2008• Guest Editor for Natural Computation, Springer, special issue dedicated non-standard computation and biological modelling, 2008• Guest Editor for International Journal of Intelligent Systems, Wiley, special issue on Nature Inspired Cooperative Strategies • in Optimisation, 2007
D. Landa-Silva:
Editorial Board for the Memetic Computing Journal (Member)•
G. Ochoa:
Guest Co-editor of a forthcoming special issue of the Journal of Heuristics on ‘Hyper-heuristics in Search and Optimisation’, 2008•
E Özcan:
Associate Editor of International Journal of Applied Metaheuristic Computing• Guest Co-editor of a forthcoming special issue of the Journal of Heuristics on ‘Hyper-heuristics in Search and Optimisation’, 2008•
S. Petrovic:
Guest Co-editor of the special issue of Annals of Operations Research on ‘Personnel Scheduling and Planning’, 2007• Editorial Board of the Yugoslav Journal of Operations Research - YUJOR (YU ISSN 0354-0243) (Member)•
R.Qu:
Guest Co-editor of a forthcoming special issue of the Journal of Scheduling on ‘Artificial Intelligence Planning and Scheduling’, 2008•
Conference/Workshop Organisation
J. Bacardit:
Co-chair of the 10th International Workshop on Learning Classifier Systems, London, July 8th, 2007• Co-chair of the 11th International Workshop on Learning Classifier Systems, Atlanta, July 13th, 2008•
A. Bargiela:
Management Board for ECMS’2007 (Member)• Management Board for ECMS’2008 (Member)•
E.K. Burke:
Co-chair of the Programme Committee of the 7th International Conference on the Practice and Theory of Automated Timetabling • (PATAT’08), held in Montreal, August 2008.
Co-chair of the Programme Committee of the IEEE 2007 Computational Intelligence in Scheduling Symposium (CIS 2007) in conjunction • with 2007 IEEE Symposium on Computational Intelligence (SSCI 2007), April 1-5, 2007, Honolulu, Hawaii, USA
25 www�asap�cs�nott�ac�uk
G. Kendall:
Technical Co-chair of the Congress on Evolutionary Computation 2007 (CEC’07), September 25-28, Singapore• Co-chair of the IEEE 2007 Computational Intelligence in Scheduling Symposium (CIS 2007) in Conjunction with 2007 IEEE Symposium • on Computational Intelligence (SSCI 2007), April 1-5, 2007, Honolulu, Hawaii, USA
Co-chair of the MISTA 2007 (The 3rd Multidisciplinary International Conference on Scheduling : Theory and Applications) conference, • 28-31 August 2007, Paris, France
N. Krasnogor:
First European Conference on Synthetic Biology (ECSB I), held in Sant Feliu de Guíxols, Spain, 2007• Second International Workshop on Nature Inspired Cooperative Strategies for Optimisation (NICSO II), held in Sardinia, Italy, 2007• Third International Workshop on Nature Inspired Cooperative Strategies for Optimisation (NICSO III), held in Tenerife, Spain, 2008• First International Symposium on Embodied Evolution (EmboEvo), held in Venice, Italy, 2007•
D. Landa-Silva:
Co-chair of the Stream on ‘Timetabling and Rostering’ at the EURO 2007 Conference held in Prague, July 2007.•
G. Ochoa:
Co-organiser of the Workshop on ‘Hyper-heuristics’, held in conjunction with the PPSN X, 2008•
E Özcan:
Co-organiser of the Workshop on ‘Hyper-heuristics’, held in conjunction with the PPSN X, 2008•
Conference Programme Committee Memberships (2007-2008)J. Bacardit: 14
R. Bai: 5
A. Bargiela: 8
E.K. Burke: 24
S. Y. Chong: 1
G. Kendall: 39
N. Krasnogor: 27
D. Landa-Silva: 5
G. Ochoa: 5
E. Özcan: 8
S. Petrovic: 12
R. Qu: 6
26 A S A P
Books 2008Bacardit J., Bernadó-Mansilla E., Butz M.V., Kovacs T., Llorà, X., and Takadama K. (eds.), Learning Classifier Systems. 10th International
Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007, London, UK, July 8, 2007,
Revised Selected Papers, Lecture Notes in Artificial Intelligence 4998, Springer, 2008.
Cutello V., Krasnogor N., Pavone M., and Pelta D. (eds.), Nature Inspired Cooperative Strategies for Optimization (NICSO 2007),
Studies in Computational Intelligence 129, Springer, 2008.
Krasnogor N., Gustafson S., Pelta D. A., and Verdegay J. L. (eds.), Systems Self-Assembly: Multi-Disciplinary Snapshops,
Volume 5, Elsevier, 2008.
2007Baptiste P., Munier A., Kendall G., and Sourd F. (eds.), Proceedings of the MISTA 2007 (The 3rd Multidisciplinary International Conference
on Scheduling : Theory and Applications), Paris, France, 2007.
Burke E., and Rudova H. (eds.), Practice and Theory of Automated Timetabling VI: Selected Revised Papers from the 6th International
Conference on the Practice and Theory of Automated Timetabling, Brno, Czech Republic – Lecture Notes in Computer Science 3867,
35-252, Springer Berlin / Heidelberg, 2006 – 2007.
Kendall G., Yao X., and Chong S. Y. (eds.), The Iterated Prisoners’ Dilemma 20 Years On, Advances in Natural Computation 4,
World Scientific, Singapore, 2007.
Kendall G., Burke E., Tan K. C. and Smith S. (eds.), Proceedings of the 2007 IEEE Symposium on Computational Intelligence
in Scheduling, 2007.
Book chapters 2008Atkin J., Burke E., J.Greenwood , and Reeson D., A Meta-Heuristic Approach to Aircraft Departure Scheduling at London Heathrow Airport, In
Computer Aided Systems in Public Transport (M.Hickman P. and S.Voss , Eds.), Lecture Notes in Economics and Mathematical Systems 600,
235-252. Springer, 2008.
Bacardit J., Bernadó-Mansilla E., and Butz M., Learning Classifier Systems: Looking Back and Glimsing Ahead, In Learning Classifier Systems.
10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007, London, UK,
July 8, 2007, Revised Selected Paper, Lecture Notes in Artificial Inteligence 4998, 1-21. Springer, 2008.
Bacardit J., and Krasnogor N., Empirical evaluation of ensemble techniques for a Pittsburgh Learning Classifier System, In Learning Classifier
Systems. 10th International Workshop, IWLCS 2006, Seattle, MA, USA, July 8, 2006, and 11th International Workshop, IWLCS 2007,
London, UK, July 8, 2007, Revised Selected Paper, Lecture Notes in Artificial Inteligence 4998, 255-268. Springer, 2008.
Bacardit J., Stout M., Hirst J. D., and Krasnogor N., Data Mining in Proteomics with Learning Classifier Systems, In Learning Classifier
Systems in Data Mining (Bull, L., Bernado-Mansilla, E. and Holmes, J., Eds.), Studies in Computational Intelligence 25/2008, 17-46.
Springer Berlin / Heidelberg, 2008.
Publications (2007-2008)
27 www�asap�cs�nott�ac�uk
Landa-Silva D., and Le K. N., A Simple Evolutionary Algorithm with Self-Adaptation For Multi-Objective Nurse Scheduling, In Adaptive and
multilevel metaheuristics (Cotta S. M., C. and Srensen K. e., Eds.), Studies in Computational Intelligence 136, 133-155. Springer, 2008.
Moratori P., Petrovic S., and Vázquez-Rodríguez A., Match-up Strategies for Job Shop Rescheduling, In New Frontiers in Applied Artificial
Intelligence (Nguyen N. T., Ed.), Lecture Notes in Artificial Inteligence 5027, 119-128. Springer-Verlag, 2008.
2007Abdullah S., Burke E. K., and McCollum B., Using a Randomised Iterative Improvement Algorithm with Composite Neighbourhood Structures for the
University Course Timetabling Problem, In Metaheuristics - Progress in Complex Systems Optimization (Doerner K. F., Gendreau M., Greistorfer P.,
Gutjahr W. J., Hartl R. F., and Reimann M., Eds.), Operations Research/Computer Science Interfaces 39, 153-169. Springer US, 2007.
Bacardit J., and Butz M. V., Data Mining in Learning Classifier Systems: Comparing XCS with GAssist In Learning Classifier Systems,
Revised Selected Papers of the International Workshop on Learning Classifier Systems 2003-2005 (Kovacs T., Llorà X., Takadama K.,
Lanzi P. L., Stolzmann W., Wilson S. W., Eds.), Lecture Notes in Artificial Intelligence 4399, 282-290. Springer-Verlag, 2007.
Bacardit J., and Garrell J. M., Bloat Control and Generalization Pressure Using The Minimum Description Length Principle for a Pittsburgh
approach Learning Classifier System, In Learning Classifier Systems, Revised Selected Papers of the International Workshop on Learning
Classifier Systems 2003-2005 (Kovacs T., Llorà X., Takadama K., Lanzi P. L., Stolzmann W., Wilson S. W., Eds.), Lecture Notes in Artificial
Intelligence 4399, 59-79. Springer-Verlag, 2007.
Bacardit J., Goldberg D. E., and Butz M. V., Improving the Performance of a Pittsburgh Learning Classifier System Using a Default Rule,
In Learning Classifier Systems, Revised Selected Papers of the International Workshop on Learning Classifier Systems 2003-2005 (Kovacs T.,
Llorà X., Takadama K., Lanzi P. L., Stolzmann W., Wilson S. W., Eds.), Lecture Notes in Artificial Intelligence 4399, 291-307. Springer-Verlag, 2007.
Bargiela A., Travel Time Estimation Through Statistical Correlation Of Inductive Loop Readings, In Crisp and Fuzzy Essays on Transportation
Research (R. Tapio Luttinen, Ed.), 323-332, HUT Publications 110, Espoo, 2007.
Baxter J. L., Burke E. K., Garibaldi J. M., and Norman M., Multi-Robot Search and Rescue: A Potential Field Based Approach, In Autonomous
Robots and Agents (Mukhopadhyay S. and Sen Gupta G., Eds.), Studies in Computational Intelligence 76, 9-16. Springer Verlag, 2007.
Beyrouthy C., Burke E. K., Landa-Silva D., McCollum B., McMullan P., and Parkes A. J., The Teaching Space Allocation Problem with Splitting,
In Practice and Theory of Automated Timetabling VI: Revised Selected papers from the 6th international conference, PATAT, Aug 30-Sep 1,
2006 (Burke E. K. and Rudova H., Eds.), Lecture Notes in Computer Science 3867, 228-247. Springer-Verlag, Brno, Czech Republic, 2007.
Chong S. Y., Humble J., Kendall G., Li J. and Yao X., The Iterated Prisoners’ Dilemma: 20 Years On, In The Iterated Prisoners’ Dilemma:
20 Years On (Kendall G., Yao X. and Chong S. Y., Eds.), Advances in Natural Computation 4, Chapter 1, 1-21, World Scientific, Singapore, 2007.
Chong S. Y., Humble J., Kendall G., Li J. and Yao X., Iterated Prisoner’s Dilemma and Evolutionary Game Theory, In The Iterated Prisoners’ Dilemma:
20 Years On (Kendall G., Yao X. and Chong S. Y., Eds.), Advances in Natural Computation 4, Chapter 2, 23-62, World Scientific, Singapore, 2007.
Chong S. Y., Humble J., Kendall G., Li J. and Yao X., Learning IPD Strategies through Coevolution, In The Iterated Prisoners’ Dilemma: 20 Years On
(Kendall G., Yao X. and Chong S. Y., Eds.), Advances in Natural Computation 4, Chapter 3, 63-87, World Scientific, Singapore, 2007.
Li L., Siepmann P., Smaldon J., Terrazas G., and Krasnogor N., Automated Self-Assembling Programming, In Systems Self-Assembly:
Multi-Disciplinary Snapshops (Krasnogor N., Gustafson S., Pelta D. A., and Verdegay J. L., Eds.), Volume 5, 281-307. Elsevier, 2007.
28 A S A P
PhD Theses 2008Atkin J., On-line decision support for take-off runway scheduling at London Heathrow airport, PhD thesis, School of Computer Science
and Information Technology, University of Nottingham, 2008.
Barteczko-Hibbert C., An Artificial Neural Network Model for the Optimisation of Diverse Domestic Energy Systems, PhD thesis, School
of Computer Science and Information Technology, University of Nottingham, 2008.
Yaakob R., Integration of a Best Population Pool and Social Learning: An Investigation in Game Playing, PhD thesis, School of Computer
Science and Information Technology, University of Nottingham, 2008.
2007Spoerer K., The Lemmings Puzzle: Computational Complexity of an Approach and Identification of Difficult Instances, PhD thesis, School
of Computer Science and Information Technology, University of Nottingham, 2007.
Moss, B., The Data Integrity Problem and Multi-Layered Document Integrity, PhD thesis, School of Computer Science and Information
Technology, University of Nottingham, 2007.
Tadrus, S., Generic Multi-Pheromone Quality of Service Routing, PhD thesis, School of Computer Science and Information Technology,
University of Nottingham, 2007.
Wang X.-Y., Fuzzy Clustering in the Analysis of Fourier Transform Infrared Spectra for Cancer Diagnosis, PhD thesis, School of Computer
Science and Information Technology, University of Nottingham, 2007
Journal Papers in PressAgafonov E., Bargiela A., Burke E. and Peytchev E., Mathematical Justification of a Heuristic for Statistical Correlation of Real-Life Time
Series, accepted for publication in European Journal of Operational Research, to appear 2009.
Aickelin U., Burke E., and Li J., Improved Squeaky Wheel Optimisation for Robust Personnel Scheduling, accepted for publication in
IEEE Transactions on Evolutionary Computation, to appear 2009.
Alcalá-Fdez J., Sánchez L., García S., del Jesus M. J., Ventura S., Garrell J., Otera J., Romero C., Bacardit J., Rivas V., and Fernández J., KEEL:
A Software Tool to Assess Evolutionary Algorithms for Data Mining Problems, accepted for publication in Soft Computing - A Fusion of
Foundations, Methodologies and Applications, to appear 2009.
Asmuni H., Burke E. K., Garibaldi J. M., McCollum B., and Parkes A. J., An Investigation of Fuzzy Multiple Heuristic Orderings in the
Construction of University Examination Timetables, accepted for publication in Computers and Operations Research, to appear 2009.
Bacardit J., Burke E., and Krasnogor N., Improving the Scalability of Rule-Based Evolutionary Learning, accepted for publication in
Memetic Computing, to appear 2009.
Bacardit J., and Krasnogor N., Performance and Efficiency of Memetic Pittsburgh Learning Classifier Systems, accepted for publication in
Evolutionary Computation, to appear 2009.
29 www�asap�cs�nott�ac�uk
Beddoe G., Petrovic S., and Li J., A Hybrid Metaheuristic Case-based Reasoning System for Nurse Nostering, accepted for publication in
Journal of Scheduling, to appear 2009.
Beyrouthy C., Burke E. K., Landa-Silva J. D., McCollum B., McMullan P., and Parkes A. J., Towards Improving the Utilisation of University
Teaching Space, accepted for publication in Journal of the Operational Research Society, to appear 2009.
Burke E.K., Hellier R.S.R., Kendall G., and Whitwell G., Irregular Packing using the Line and Arc No-Fit Polygon, accepted for publication in
Operations Research, to appear 2009.
Burke E.K., Kendall G., and Whitwell G., A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock Cutting
Problem, accepted for publication in INFORMS Journal on Computing, to appear 2009.
Huy N. Q., Soon O. Y., Hiot L. M., and Krasnogor N., Adaptive Cellular Memetic Algorithm, accepted for publication in Evolutionary Computation,
to appear 2009.
Li J., Aickelin U., and Burke E., A Component Based Heuristic Search Method with Evolutionary Eliminations for Hospital Personnel
Scheduling, accepted for publication in INFORMS Journal on Computing, to appear 2009.
Li J., Garibaldi J., and Krasnogor N., Automated Self-Assembly Programming Paradigm: The Impact of Network Topology, accepted for
publication in International Journal of Intelligent Systems, to appear 2009.
Li J., and Kendall G., A Strategy with Novel Evolutionary Features for the Iterated Prisoner’s Dilemma, accepted for publication in
Evolutionary Computation, to appear 2009.
McCollum B., McMullan P, Paechter B., Lewis R., Schaerf A., Di Gaspero L., Parkes A. J., Qu R., and Burke E., Setting the Research Agenda in
Automated Timetabling: The Second International Timetabling Competition, accepted for publication in INFORMS Journal of Computing, to
appear 2009.
Melville J. L., Burke E. K., and Hirst J. D., Machine Learning in Virtual Screening, accepted for publication in Combinatorial Chemistry &
High Throughput Screening, to appear 2009.
Özcan E., and Başaran C., A Case Study of Memetic Algorithms for Constraint Optimization, accepted for publication in
Soft Computing - A Fusion of Foundations, Methodologies and Applications, to appear 2009.
Ouelhadj D., and Petrovic S., Survey of Dynamic Scheduling in Manufacturing Systems, accepted for publication in Journal of Scheduling,
to appear 2009.
Qu R., Burke E., and McCollum B., Adaptive Automated Construction of Hybrid Heuristics for Exam Timetabling and Graph Colouring
Problems, accepted for publication in European Journal of Operational Research, to appear 2009.
Qu R., Burke E., McCollum B., Merlot L., and Lee S., A Survey of Search Methodologies and Automated System Development for
Examination Timetabling, accepted for publication in Journal of Scheduling, to appear 2009.
Stout M., Bacardit J., Hirst J., Smith R., and Krasnogor N., Prediction of Topological Contacts in Proteins Using Learning Classifier Systems,
accepted for publication in Soft Computing - A Fusion of Foundations, Methodologies and Applications, to appear 2009.
30 A S A P
Xiao K., Ho S.H., and Bargiela A., Brain MRI Tumor Segmentation with the Assistance of Lateral Ventricular Deformation Estimation, accepted
for publication in Journal of Australasian College of Physical Scientists and Engineers in Medicine, to appear 2009.
Journal Papers 2008Atkin J., E.K.Burke , Greenwood J., and Reeson D., On-line Decision Support for Take-off Runway Scheduling with Uncertain Taxi Times at
London Heathrow Airport, Journal of Scheduling, 11(5):323-346, 2008.
Ayob M., and Kendall G., A Survey of Surface Mount Device Placement Machine Optimisation: Machine Classification, European Journal of
Operational Research, 186(3):893-914, 2008.
Bai R., and Kendall G., A Model for Fresh Produce Shelf Space Allocation and Inventory Management with Freshness Condition Dependent
Demand, INFORMS Journal on Computing, 20(1):78-85, 2008.
Bai R., Burke E. K., and Kendall G., Heuristic, Meta-Heuristic and Hyper-Heuristic Approaches for Fresh Produce Inventory Control and Shelf
Space Allocation, Journal of the Operational Research Society, 59(10): 1387-1397, 2008.
Bargiela A., and Pedrycz W., Toward a Theory Of Granular Computing for Human-Centred Information Processing, IEEE Trans. on Fuzzy
Systems, vol. 16, 2, 320-330, 2008.
Binner J.M., Gazely A.M., and Kendall G., Evaluating the Performance of a EuroDivisia Index Using Artifcial Intelligence Techniques,
International Journal of Automation and Computing, 5(1):58-62, 2008.
Burke E. K., Curtois T. E., Post G., Qu R., and Veltman B., A Hybrid Heuristic Ordering and Variable Neighbourhood Search for the Nurse
Rostering Problem, European Journal of Operational Research, 188:330-341, 2008.
Burke E. K., Dror M., and Orlin J., Scheduling Malleable Tasks with Interdependent Processing Rates: Comments and Observations,
Discrete Applied Mathematics, 156(5):620-626, 2008.
Chong S. Y., Tino P., and Yao X., Measuring Generalization Performance in Co-evolutionary Learning, IEEE Transactions on Evolutionary
Computation, 12(4):479-505, 2008.
Gheorghe M., Krasnogor N., and Camara M., P Systems Applications to Systems Biology, Biosystems, 91:435-437, 2008.
Kendall G., Scheduling English Football Fixtures Over Holiday Periods, Journal of the Operational Research Society, 59(6):743-755, 2008.
Kendall G., Parkes A. J., and Spoerer K., A Survey of NP-Complete Puzzles, International Computer Games Association Journal, 31(1):13-34, 2008.
Krasnogor N. and Smith J., Memetic Algorithms: The Polynomial Local Search Complexity Theory Perspective, Journal of Mathematical
Modelling and Algorithms, 7:3-24, 2008.
Oakley M. T., Barthel D., Bykov Y., Garibaldi J. M., Burke E. K., Krasnogor N., and Hirst J. D., Search strategies in structural bioinformatics,
Current Protein and Peptide Science, Bentham Science Publishers, 9(3):260-274, 2008.
31 www�asap�cs�nott�ac�uk
Oates R., Kendall G., and Garibaldi J. M., Frequency Analysis for Dendritic Cell Population Tuning: Decimating the Dendritic Cell,
Evolutionary Intelligence, 1(2):145-157, 2008.
Petrovic S., Fayad C., and Petrovic D., Sensitivity Analysis of a Fuzzy Multiobjective Scheduling Problem, International Journal of
Production Research, 46(12):3327-3344, 2008.
Petrovic S., Fayad C., Petrovic D., Burke E., and Kendall G., Fuzzy Job Shop Scheduling with Lot-sizing, Annals of Operations Research,
159(1):275-292, 2008.
Shah A. A., Barthel D., Lukasiak P., Blazewicz J., and Krasnogor N., Web & Grid technologies in Bioinformatics, Computational and Systems
Biology: A Review, Current Bioinformatics, 3:10-31, 2008.
Stout M., Bacardit J., Hirst J., and Krasnogor N., Prediction of Recursive Convex Hull Class Assignments for Protein Residues, Bioinformatics,
24(7):916-923, 2008.
Tomassini M., Verel S., and Ochoa G., Complex Networks Analysis of Combinatorial Spaces: The NK Landscape Case, Physical Review E,
78(6):66114 - 66123, 2008.
Wang W., Barnaghi P. and Bargiela A., Search with Meanings: An Overview of Semantic Search Systems, Int. J. Communications of SIWN,
3, 76-82, 2008.
2007Abdullah S., Ahmadi S., Burke E., and Dror M., Investigating Ahuja-Orlin’s Large Neighbourhood Search Approach for Examination
Timetabling, OR Spectrum, 29(2):351-372, 2007.
Abdullah S., Ahmadi S., Burke E. K., Dror M., and McCollum B., A Tabu Based Large Neighbourhood Search Methodology for the Capacitated
Examination Timetabling Problem, Journal of the Operational Research Society, 58(11):1494-1502, 2007.
Aickelin U., Burke E. K., and Li J., An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering,
Journal of the Operational Research Society, 58(12):1574-1585, 2007.
Aickelin U., and Li J., An Estimation of Distribution Algorithm for Nurse Scheduling, Annals of Operations Research, 155(1):289-309, 2007.
Atkin J. A., Burke E. K., Greenwood J., and Reeson D., Hybrid Meta-heuristics to Aid Runway Scheduling at London Heathrow Airport,
Transportation Science, 41(1):90-106, 2007.
Bargiela A., and Pedrycz W., Toward a Theory of Granular Computing for Human-Centered Information Processing, IEEE Transactions On
Fuzzy Systems, 16(2):320-330, 2007.
Bargiela A., Pedrycz W., and Nakashima T., Multiple Regression with Fuzzy Data, Fuzzy Sets and Systems, 158:2169-2188, 2007.
Barthel D., Hirst J., Blazewicz J., Burke E., and Krasnogor N., Procksi: A Decision Support System For Protein (Structure) Comparison,
Knowledge, Similarity and Information, BMC Bioinformatics, 8:416, 2007.
Beddoe G., and Petrovic S., Enhancing Case-based Reasoning For Personnel Rostering With Selected Tabu Search Concepts,
Journal of the Operational Research Society, 58(12):1586-1598, 2007.
32 A S A P
Bernardini F., Gheorghe M., and Krasnogor. N., Quorum sensing P systems, Theoretical Computer Science, 371(1-2):20-33, 2007.
Blazewicz J., Burke E. K., Kazprzak M., Kovyalov A., and Kovyalov M. Y., The Simplified Partial Digest Problem: Enumerative and Dynamic
Programming Algorithms, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 4(4):668-680, 2007.
Burke E. K., Hellier R. S. R., Kendall G., and Whitwell G., Complete and Robust No-Fit Polygon Generation for the Irregular Stock Cutting
Problem, European Journal of Operational Research, 179(1):27-49, 2007.
Burke E. K., McCollum B., Meisels A., Petrovic S., and Qu R., A Graph-Based Hyper-Heuristic for Educational Timetabling Problems,
European Journal of Operational Research, 176:177-192, 2007.
Chong S. Y., and Yao X., Multiple Choices and Reputation in Multiagent Interactions, IEEE Transactions on Evolutionary Computation,
11(6):689-711, 2007.
Dowsland K., Gilbert M., and Kendall G., A Local Search Approach to a Circle Cutting Problem Arising in the Motor Cycle Industry, Journal of
the Operational Research Society, vol. 58, 429-438, 2007.
Dowsland K. A., Soubeiga E., and Burke E. K., A Simulated Annealing Hyper-Heuristic for Determining Shipper Sizes,
European Journal of Operational Research, 179(3):759-774, 2007.
Jaskowski W., Blazewicz J., Lukasiak P., Milostan M., and Krasnogor N., 3D-Judge - A Metaserver Approach to Protein Structure Prediction,
Foundations of Computing and Decision Sciences, 31(1), 2007.
Kendall G., and Su Y., Imperfect Evolutionary Systems, IEEE Transactions on Evolutionary Computation, 1(3):294-307, 2007.
Landa-Silva J. D., and Burke E. K., Asynchronous Cooperative Local Search for the Office Space Allocation Problem,
INFORMS Journal on Computing, 19(4):575-587, 2007.
Ochoa G., Villasana M., and Burke E. K., An Evolutionary Approach to Cancer Chemotherapy Scheduling,
Genetic Programming and Evolvable Machines, 8(4):301-318, 2007.
Ong Y., Krasnogor N., and Ishibuchi H., Special Issue on Memetic Algorithms, IEEE Transactions on Systems, Man and Cybernetics, Part B,
37(1):2-5, 2007.
Petrovic D., Duenas A., and Petrovic S., Decision Support Tool for Multi-Objective Job Shop Scheduling Problems with Linguistically
Quantified Decision Functions, Decision Support Systems, 43(4):1527-1538, 2007.
Petrovic S., Yang Y., and Dror M., Case-Based Selection of Initialisation Heuristics for Metaheuristic Examination Timetabling,
Expert Systems with Applications, 33(3):772-785, 2007.
Siepmann P., Martin C., Vancea I., Moriarty P., and Krasnogor N., A Genetic Algorithm Approach to Probing the Evolution of Self-Organised
Nanostructured Systems, Nano Letters, 7(7):1985-1990, 2007.
Terrazas G., Siepmann P., Kendall G., and Krasnogor N., An Evolutionary Methodology for the Automated Design of Cellular Automaton-
based Complex Systems, Journal of Cellular Automata, 2:77-102, 2007.
The ASAP group also published many peer reviewed conference papers from 2007-2008. Please refer to the following page to view the full list:
http://www�asap�cs�nott�ac�uk/newpublications/twiki/bin/view/ASAP/ConferencePapers/
33 www�asap�cs�nott�ac�uk
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snog
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mar
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(EP/
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£515
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Colla
bora
ting
with
the
Cent
re F
or B
iom
olec
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Scie
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, UoN
1/9/
2007
to 3
1/8/
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£515
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pro
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inve
stig
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a n
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sem
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cel
lula
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Prob
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Kra
snog
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C
(GR
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534/
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£209
,589
Colla
bora
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with
the
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emis
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and
Dr.
R. S
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, UCL
1/3/
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to 2
9/2/
2008
£209
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betw
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EPSR
C
(EP/
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1847
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£77,
658
1/
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005
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0/9/
2008
£77,
658
This
is a
n ID
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Fact
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proj
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colla
bora
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with
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Uni
vers
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of
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lasg
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Com
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urke
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di
BB
SRC
(BB
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1764
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£66,
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1/
2/20
05
to 3
1/7/
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£66,
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This
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Tow
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Mor
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EP
SRC
(EP/
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£204
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£204
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Stra
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Mid
land
Sof
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Lim
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12/8
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8
to 1
5/2/
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£111
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This
pro
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is a
Kno
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Tran
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Par
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Nov
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(EP/
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£268
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£95,
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£363
,315
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oven
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Uni
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CHEL
Lnet
: A U
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Inve
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C
(EP/
D02
3343
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£81,
737
1/
10/2
005
to
31/3
/200
9
£81,
737
This
is a
n ID
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Fact
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proj
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colla
bora
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with
the
Uni
vers
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of
Edin
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h, G
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(£3
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Land
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(£31
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Kra
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£869
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ndal
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Bur
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£6,1
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Lead
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Indu
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(£4
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Oth
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£406
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BB
SRC
(£56
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EU (
£758
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EPSR
CTa
rget
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alls
(£5,
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EPSR
Res
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Mod
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P F
unds
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Sour
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(Gra
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8)
ASAP PersonnelAcademic Staff
Prof Edmund K� Burke
Head of Group Professor of Computer Science
(0115) 95 14206 ekb@cs.nott.ac.uk www.cs.nott.ac.uk/~ekb/
Dr Dario Landa-Silva
Lecturer
(0115) 84 66522 jds@cs.nott.ac.uk www.cs.nott.ac.uk/~jds/
Prof Graham Kendall
Deputy Head of Group Dunford Professor of Computer Science
(0115) 84 66514 gxk@cs.nott.ac.uk www.cs.nott.ac.uk/~gxk/
Dr Ender Özcan
Science and Innovation Lecturer
(0115) 84 66569 exo@cs.nott.ac.uk www.cs.nott.ac.uk/~exo/
Dr Jaume Bacardit
Lecturer
(0115) 84 67044 jqb@cs.nott.ac.uk www.cs.nott.ac.uk/~jqb/
Dr Andrew Parkes
Science and Innovation Lecturer
Room: B81 (0115) 95 14210 ajp@cs.nott.ac.uk www.cs.nott.ac.uk/~ajp/
Dr Ruibin Bai (UNMC)
Lecturer
Ningbo Campus, China +86 574 8818 0278 rzb@cs.nott.ac.uk www.cs.nott.ac.uk/~rzb/
Prof Sanja Petrovic
Professor of Computer Science
Room: C83 (0115) 95 14222 sxp@cs.nott.ac.uk www.cs.nott.ac.uk/~sxp/
Prof Andrzej Bargiela (UNMC)
Professor of Computer Science (UNMC)
(0115) 84 67279 abb@cs.nott.ac.uk www.cs.nott.ac.uk/~abb/
Dr Rong Qu
Lecturer
Room: C43 (0115) 84 66503 rxq@cs.nott.ac.uk www.cs.nott.ac.uk/~rxq/
Dr Natalio Krasnogor
Associate Professor
(0115) 84 67592 nxk@cs.nott.ac.uk www.cs.nott.ac.uk/~nxk/
Dr Siang Yew Chong (UNMC)
Lecturer
Malaysia Campus +6 03 8924 8148 Siang-Yew.Chong@nottingham.edu.my baggins.nottingham.edu.my/~khczcsy/
38 A S A P
Administrative Staff
Debbie Pitchfork
ASAP Research Group Project Manager
(0115) 84 66543 dap@cs.nott.ac.uk
Nick Poxon
ASAP Research Support Coordinator
(0115) 84 66504 nzp@cs.nott.ac.uk
Ebru Tasci
Administrative Assistant
(0115) 84 67700 est@cs.nott.ac.uk
Dr Gabriela OchoaDr Jason Atkin
Senior Research Fellows
39 www�asap�cs�nott�ac�uk
Dr Yuri Bykov
Rupa Jagannathan
Dr German Terrazas Angulo
Dr HongQing Cao
Dr Hui Li
Dr Jamie Twycross
Dr Tim Curtois
Dr Jingpeng Li
Dr Antonio Vazquez
Dr Geert De Maere
Dr Jiawei Li
Dr Glenn Whitwell
Dr Matthew Hyde
Dr Djamila Ouelhadj
Research Associates
ASAP Personnel (continued)
40 A S A P
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Pilar Camano Sobrino
Elkin Castro Juan Pedro Castro Gutierrez
Qun Bo Chen Jack Chaplin
Ha Duong Maria Franco Enrico Glaab Sven Groenemeyer Qiang Guo
Nor Hayati Hamid
Fang He Rob Hellier Joe Henry Obit Ebrahim Kamrani
Mohd Nizam Mohmad Kahar
Khoi Le Rahul Singh Majhail
Jakub Marecek Nishikant Mishra
PhD Students
41 www�asap�cs�nott�ac�uk
Abdullah Muhammed
Patrick Barbosa Moratori
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Syariza Abdul Rahman
Pedro Rocha Shamsudin MD Sarif
Peter Siepmann James Smaldon
Amr Soghier Mike Stout Adam Sweetman Azhar Ali Shah Syed
Özgür Ülker
Pawel WideraNadarajen Veerapen
Ying Xu
PhD Students
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See www.nottingham.ac.uk/ComputerScience/About/FindingUs.aspx/ for directions
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Thomas Runarsson
Müjgan Sağir Minaya Villasana
John Woodward
Visiting Fellows/Associated Staff
How to find us
43 www�asap�cs�nott�ac�uk
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