Rhyd Lewis
Rhyd Lewis

Dr. Rhydian Lewis BSc, PhD, FHEA.

Senior Lecturer in Operational Research,
Cardiff School of Mathematics,
Cardiff University, Cardiff, CF24 4AG, WALES.
Tel: +44(0)29 208 74856
Email: LewisR9@cf.ac.uk

 

Link to school webpage

Welcome to my webpage. Below you will find information about my teaching, research, publications, and presentations. Some useful research resources, code, and problem instances can also be found by following the links below.

If you would like to contact me or arrange a visit, please use the email address above.

Research and Teaching

Overview

I am a senior lecturer at Cardiff School of Mathematics and a member of the department's Operational Research Group. I am the director of two MSc programs in the School of Mathematics: the MSc in Operational Research and Applied Statistics, and the MSc in Operational Research, Applied Statistics and Financial Risk. More information can be found here. In addition, here is a nice short video highlighting some of the research activities carried by the School.

I am an associate editor for the International Journal of Metaheuristics, having co-founded the journal in 2007. I am also a member of the program committees for Evolutionary Computation in Combinatorial Optimisation, the Practice and Theory of Automated Timetabling, GECCO, and the International Metaheuristics Conference series. From 2015 to 2017 I led a two-year £250k Health and Care Research Wales-funded project entitled "Prudent Elective Surgery Scheduling: A Whole Systems Approach".

Teaching

I am currently teaching the following modules in the School of Mathematics: MA2900 Problem Solving and MA3602 Algorithms and Heuristics. Previous modules include MAT002 Statistical Methods; MA0276 Visual Basic for Operational Research; MAT004 Computational Methods; and MA0105 Foundations of Probability. I also previously taught the following modules at Cardiff Business School: BSP658 Business Statistics; BS0511 Quantitative Methods for Business; and BS1501 Applied Statistics and Mathematics for Economics and Business. I am a fellow of the Higher Education Academy.

Research Interests

- The application and analysis of metaheuristic and integer programming algorithms;
- Algorithmic graph theory;
- Graph colouring (including vertex colouring, edge colouring and happy colouring);
- Operating theatre scheduling;
- School bus routing;
- Automated timetabling (course and exam) and related problems;
- Grouping/Partitioning problems;
- Sports timetabling, particularly round-robin scheduling;
- Solving sudoku problems with metaheuristics;
- Bin-packing, trapezoid (trapezium) packing, and the equal-piles problem;
- Vehicle routing and arc routing, particularly dynamic variants of the problem;

Publications and Presentations

Books, Journal Articles and Chapters

2018

  • Lewis, R., K. Smith-Miles, and K. Phillips (2018) 'The School Bus Routing Problem: An Analysis and Algorithm'. In Combinatorial Algorithms (Lecture Notes in Computer Science vol. 10765), Springer, pp. 287-298.
  • Hardy, B., R. Lewis, and J. Thompson (2017) 'Tackling the Edge Dynamic Graph Colouring Problem with and without Future Adjacency Information'. Journal of Heuristics, vol. 24(3), pp. 321-343.
  • 2017

  • Lewis, R. and P. Holborn (2017) 'How to Pack Trapezoids: Exact and Evolutionary Algorithms'. IEEE Transactions on Evolutionary Computation, vol. 21(3), pp. 463-476.
  • 2016

  • Lewis, R. (2016) 'Graph Colouring: An Ancient Problem with Modern Applications'. Impact, Spring 2016 (3), pp. 47-50, issn:2058-8030.
  • Lewis, R. and F. Carroll (2016) 'Creating Seating Plans: A Practical Application'. Journal of the Operational Research Society, vol. 67(11), pp. 1353-1362.
  • Padungwech, W., J. Thompson, and R. Lewis (2016) 'Investigating Edge-Reordering Procedures in a Tabu Search Algorithm for the Capacitated Arc Routing Problem'. In Hybrid Metaheuristics (Lecture Notes in Computer Science vol. 9668), Berlin: Springer-Verlag, pp. 62-74.
  • Hardy, B., R. Lewis, and J. Thompson (2016) 'Modifying Colourings Between Time-steps to Tackle Changes in Dynamic Random Graphs'. In Evolutionary Computation in Combinatorial Optimisation (Lecture Notes in Computer Science vol. 9595), Berlin: Springer-Verlag, pp. 186-201.
  • 2015

  • Lewis, R. (2015) A Guide to Graph Colouring: Algorithms and Applications. Berlin, Springer. ISBN: 978-3-319-25728-0.
  • Lewis, R. and J. Thompson (2015) 'Analysing the Effects of Solution Space Connectivity with an Effective Metaheuristic for the Course Timetabling Problem'. European Journal of Operational Research, vol. 240, pp. 637-648.
  • Lewis, R. (2015) 'Graph Coloring and Recombination'. Springer Handbook of Computational Intelligence, J. Kacprzyk and W. Pedrycz (Eds.), pp. 1239-1254, ISBN: 978-3-662-43504-5.
  • 2014

  • Smith-Miles, K., D. Baatar, B. Wreford, and R. Lewis (2014) 'Towards Objective Measures of Algorithm Performance Across Instance Space'. Computers and Operations Research, vol. 45, pp. 12-24.
  • John, M., C. Mumford, and R. Lewis (2014) 'An Improved Multi-objective Algorithm for the Urban Transit Routing Problem'. In Evolutionary Computation in Combinatorial Optimisation (Lecture Notes in Computer Science vol. 8600), Berlin: Springer-Verlag, pp. 49-60.
  • 2013

  • Carroll, F. and R. Lewis (2013) 'The "Engaged" Interaction: Important Considerations for the HCI Design and Development of a Web Application for Solving a Complex Combinatorial Optimization Problem'. World Journal of Computer Application and Technology, vol. 1(3), pp. 75-82.
  • 2012

  • Holborn, P. L., J. M. Thompson, and R. Lewis (2012) 'Combining Heuristic and Exact Methods to Solve the Vehicle Routing Problem with Pickups, Deliveries and Time Windows'. In Evolutionary Computation in Combinatorial Optimisation (Lecture Notes in Computer Science vol. 7245), Berlin: Springer-Verlag, pp. 63-74.
  • Lewis, R., J. Thompson, C. Mumford, and J. Gillard (2012) 'A Wide-Ranging Computational Comparison of High-Performance Graph Colouring Algorithms'. Computers and Operations Research, vol. 39(9), pp. 1933-1950. (This work's companion paper can be found here; the source code can be found here.)
  • Song, X., R. Lewis, J. Thompson, and Y. Wu (2012) 'An Incomplete m-Exchange Algorithm for Solving the Large-scale Multi-Scenario Knapsack Problem'. Computers and Operations Research, vol. 39(9), pp. 1988-2000.
  • Lewis, R. (2012) 'A Time-Dependent Metaheuristic Algorithm for Post Enrolment-based Course Timetabling'. Annals of Operations Research, vol. 194(1), pp. 273-289.
  • 2011

  • Lewis, R., X. Song, K. Dowsland, and J. Thompson (2011) 'An Investigation into two Bin Packing Problems with Ordering and Orientation Implications'. European Journal of Operational Research, vol. 213, pp. 52-65.
  • Lewis, R. and E. Pullin (2011) 'Revisiting the Restricted Growth Function Genetic Algorithm for Grouping Problems'. Evolutionary Computation, vol. 19(4), pp. 693-704.
  • Lewis, R. and J. Thompson (2011) 'On the Application of Graph Colouring Techniques in Round-Robin Sports Scheduling'. Computers and Operations Research, vol. 38(1), pp. 190-204.
  • 2010

  • McCollum, B., A. Schaerf, B. Paechter, P. McMullan, R. Lewis, A. Parkes, L. Di Gaspero, R. Qu, and E. Burke (2010) 'Setting The Research Agenda in Automated Timetabling: The Second International Timetabling Competition'. INFORMS Journal on Computing, vol. 22(1), pp. 120-130.
  • Song, X., C. Chu, R. Lewis, Y. Nie, and J. Thompson (2010) 'A Worst Case Analysis of a Dynamic Programming-based Heuristic Algorithm for 2D Unconstrained Guillotine Cutting'. European Journal of Operational Research, vol. 202(2), pp. 368-378.
  • 2009

  • Lewis, R. (2009) 'A General-Purpose Hill-Climbing Method for Order Independent Minimum Grouping Problems: A Case Study in Graph Colouring and Bin Packing'. Computers and Operations Research, vol. 36(7), pp. 2295-2310.
  • 2008

  • Lewis, R. (2008) 'A Survey of Metaheuristic-based Techniques for University Timetabling Problems'. OR Spectrum, vol. 30(1), pp. 167-190.
  • 2007

  • Lewis, R. (2007) 'Metaheuristics can Solve Sudoku Puzzles'. Journal of Heuristics, vol. 13 (4), pp. 387-401.
  • Lewis, R., and B. Paechter. (2007) 'Finding Feasible Timetables Using Group-Based Operators'. IEEE Transactions on Evolutionary Computation, vol. 11(3), pp. 397-413.
  • Lewis, R., B. Paechter, and O. Rossi-Doria (2007) 'Metaheuristics for University Course Timetabling'. In Evolutionary Scheduling (Studies in Computational Intelligence, vol. 49), K. Dahal, Kay Chen Tan, P. Cowling (Eds.) Berlin: Springer-Verlag, pp. 237-272.
  • Lewis, R. (2007) 'On the Combination of Constraint Programming and Stochastic Search: The Sudoku Case'. In Hybrid Metaheuristics (Lecture Notes in Computer Science vol. 4771) T. Bartz-Beielstein, M. Aguilera, C. Blum, B. Naujoks, A. Roli, G. Rudolph, and M. Sampels (Eds.) Berlin: Springer-Verlag, pp. 96-107.
  • 2006

  • Lewis, R. (2006) 'Metaheuristics for University Course Timetabling'. Doctoral Thesis, Napier University, Edinburgh, Scotland.
  • 2005

  • Lewis, R. and B. Paechter (2005). 'Application of the Grouping Genetic Algorithm to University Course Timetabling'. In Evolutionary Computation in Combinatorial Optimisation (Lecture Notes in Computer Science vol. 3448), J. Gottlieb and G. Raidl (Eds.), Berlin: Springer-Verlag, pp. 144-153.
  • Selected Peer-Reviewed Conference Papers and Technical Reports

  • Lewis, R. (2018) 'Two Example Optimisation Problems from the World of Education'. In Keynote Papers of the OR Society Annual Conference (OR60), A. Kheiri (Ed.), Lancaster, England, pp. 94-98. ISBN: 978-0-903440-64-6.
  • Kheiri, A., E. Ozcan, R. Lewis, and J. Thompson (2016) 'A Sequence-based Selection Hyper-heuristic: A Case Study in Nurse Rostering'. In PATAT 2016, Proceedings of the 11th International Conference on the Practice and Theory of Automated Timetabling, Udine, Italy, August 23–26, 2016, pp. 503-505.
  • Rowse, E., R. Lewis, P. Harper, and J. Thompson (2015) 'Applying Set Partitioning Methods in the Construction of Operating Theatre Schedules'. In Proceedings of the International Conference on Theory and Practice in Modern Computing 2015, Las Palmas de Gran Canaria, Spain, pp. 133-140. ISBN: 978-989-8533-39-5. (Recipient of the Outstanding Paper Award.)
  • Cooper, I., M. John, R. Lewis, C. Mumford and A. Olden (2014) 'Optimising large scale public transport network design problems using mixed-mode parallel multi-objective evolutionary algorithms'. In Proceedings of the 2014 IEEE Congress on Evolutionary Computation, Beijing, China, pp. 2841-2848. ISBN: 978-1-4799-6626-4.
  • Lewis, R. (2013) 'Constructing Wedding Seating Plans: A Tabu Subject'. Proceedings of the International Conference on Genetic and Evolutionary Methods (GEM'13), H. Arabnia et al. (Eds), pp. 24-32. ISBN: 1-60132-245-3.
  • Lewis, R., B. Paechter, and B. McCollum (2007) 'Post Enrolment based Course Timetabling: A Description of the Problem Model used for Track Two of the Second International Timetabling Competition'. Cardiff Working Papers in Accounting and Finance A2007-3, Cardiff Business School, Cardiff University, Wales, Aug. 2007. ISSN: 1750-6658.
  • Lewis, R. (2008) 'A Time-Dependent Metaheuristic Algorithm for Post Enrolment-based Course Timetabling'. In PATAT 2008, Proceedings of the 7th International Conference on the Practice and Theory of Automated Timetabling. (A more recent version of this paper, published in Annals of Operations Research, is available here.)
  • Lewis, R. and B. Paechter (2005). 'An Empirical Analysis of the Grouping Genetic Algorithm: The Timetabling Case'. In Proceedings of the 2005 IEEE World Congress on Evolutionary Computation, Edinburgh, Scotland, pp. 2856-2863, ISBN: 0-7803-9363-5.
  • Lewis, R. and B. Paechter (2004). 'New Crossover Operators for Timetabling with Evolutionary Algorithms'. In Proceedings of the 5th International Conference on Recent Advances in Soft Computing (RASC 2004), A. Lofti (Ed.), Nottingham, England, pp. 189-195. ISBN: 1-84233-110-8.
  • Selected Presentations

  • Two Example Optimisation Problems from the World of Education. Keynote talk given at the OR Society Annual Conference (OR60), Lancaster University, Lancaster, September 2018.
  • Bin Packing with Trapezia: Methods and Applications. Talk given at the 15th Euroean Special Interest Group on Cutting and Packing (ESICUP) Workshop, Zoetermeer, Netherlands, May 2018.
  • Applying Set Partitioning Methods in the Construction of Operating Theatre Schedules. Talk given at the International Conference on Theory and Practice in Modern Computing (TPMC) 2015, Las Palmas de Gran Canaria, July 2015.
  • A Survey of Various High-Performance Graph Colouring Algorithms and Related Timetabling Issues. Keynote talk given at the OR Society Annual Conference (OR53), Nottingham University, Nottingham, September 2011.
  • An Investigation into Trapezoid Packing. Talk given at the OR Society Annual Conference (OR52), Royal Holloway University, London, September 2010.
  • On the Application of Graph Colouring techniques in Round-Robin Sports Scheduling. Invited talk given at a meeting of the LANCS Advisory Board in London, May 2010.
  • A Time-Dependent Metaheuristic Algorithm for Post Enrolment-based Course Timetabling. Talk given at the 7th International Conference on the Practice and Theory of Automated Timetabling, Montreal, August 2008.
  • On the Combination of Constraint Programming and Stochastic Search: The Sudoku Case. Talk given at Hybrid Metaheuristics, Dortmund, April 2007.
  • An Introduction to Metaheuristic Algorithms and the Problems they (try to) Solve. Keynote talk given at the British Computing Society Lecture, Cardiff Business School, October 2008.
  • Resources

    Graph Colouring:

    Illustration of the Graph Colouring ProblemGraph colouring is the task of painting all vertices of a graph so that (a) all pairs of adjacent vertices are assigned different colours, and (b) the number of colours used is minimal. For example, the image on the right shows a solution using just three colours, which is actually the minimum for this particular graph. The graph colouring problem has applications in many practical areas of operations research including university timetabling, sports scheduling, creating seating plans, and solving sudoku puzzles. It is also strongly related to the Four Colour Theorem, which was previously one of the most famous unsolved problems in all of mathematics.

    Some short introductory videos into the field of graph colouring can be found at the following links:

    - An Introduction to Graph Colouring;
    - Constructive Algorithms for Graph Colouring;
    - Practical Applications of Graph Colouring.

    For further information on graph colouring and its applications, please refer to my 2015 book A Guide to Graph Colouring: Algorithms and Applications, ISBN: 978-3-319-25728-0. The suite of graph colouring algorithms used in this book is available here. These have been coded in C++, and the resource also contains compilation and usage instructions. Information on these algorithms can also be found in the paper: "Lewis, R., J. Thompson, C. Mumford, and J. Gillard (2012) 'A Wide-Ranging Computational Comparison of High-Performance Graph Colouring Algorithms'. Computers and Operations Research, vol. 39(9), pp. 1933-1950".

    A useful bibliography on the graph colouring problem, maintained by Marco Chiarandini and Stefano Gualandi, can be found here. In addition, information on upper bounds for some well-known problem instances can be found here.

    School Bus Routing:

    Illustration of the school bus routing problemThe school bus routing problem involves compiling a list of eligible students and then efficiently organising their transport to school. This process requires the selection of a suitable set of pick-up points (bus stops), the assignment of students to these pick-up points, and then the design of bus routes that visit these stops while getting students to school on time. In doing this, four factors should be considered.: (1) We should try to reduce economic costs by minimising the number of vehicles used; (2) Bus journeys should not be too long for students; (3) Pick-up points should be close to people’s houses; (4) Buses should not be over-filled.

    This problem is an interesting mix of vehicle routing, set covering, and the bin packing problem and it can be very difficult to find solutions for. Some of my publications above have looked at producing effective approximation algorithms for this problem. Further details, including visualisations of problems and a suite of benchmark problem instances, can also be found on a dedicated webpage here.

    Happy Colouring and Graph Homophily:

    Illustration of the Maximum Happy Vertices ProblemAnother problem that involves colouring vertices in a graph is the Maximum Happy Vertices problem. Here, we are given a graph in which some of the vertices are already coloured (as with the left graph in the figure). Our task is to then colour the remaining vertices so that they are given the same colour as all of their neighbours. In the example solution on the right, we see that most vertices are "happy" because they are the same colour as their neighbours. However, because of the colourings specified in the original graph, it is not possible for all vertices to be happy. (Unhappy vertices are marked with a "U".) This sort of problem is useful in identifying community structures in social networks, amongst other things.

    Trapezoid Packing:

    Example of a Trapezoid Packing ProblemThe Trapezoid Packing Problem is closely related to the one-dimensional bin packing problem. Here, items are trapezoidal in shape with a fixed height and variable width. A practical application arises in the roofing industry, where large numbers of roof trusses of different lengths and different "end angles" have to be cut from boards of a given length such that wastage is minimsed. Though similar to one-dimensional bin packing, the problem is complicated by the fact that the ends of the trapezoids need to be "nested" so that wastage between consecutive shapes is kept to a minimum. This problem was first introduced and analysed in the 2011 paper 'An Investigation into two Bin Packing Problems with Ordering and Orientation Implications'. The problem instances used in this paper can be downloaded here.

    In our 2016 publication 'How to Pack Trapezoids: Exact and Evolutionary Algorithms' significant improvements to the results reported in the 2011 paper were achieved by (a) designing and making use of an exact polynomial-time algorithm for optimally packing a set of fixed-height trapezoids into a single bin, and (b) by using a number of specialised evolutionary-based methods when packing across multiple bins. The source code and a detailed breakdown of the results of this work is available here.

    Timetabling:

    Some of my research has focussed on the production of efficient algorithms for timetabling problems. A large number of timetabling problem instances are available for this problem on the website of the Second International Timetabling Competition (ITC2007). Some hard timetabling instances can also be found here. This latter set contains sixty problem instances of varying sizes that are intended to be more difficult than the competition instances with regards to both finding feasibility and satisfying soft constraints. Source code and results for the algorithm presented in Lewis, R. and J. Thompson (2015) 'Analysing the Effects of Solution Space Connectivity with an Effective Metaheuristic for the Course Timetabling Problem'. European Journal of Operational Research, vol. 240, pp. 637-648 can be found here

    Constructing Operating Theatre Schedules:

    Operating Theatre SchedulingAn important part of our £250k Health and Care Research Wales-funded project "Prudent Elective Surgery Scheduling: A Whole Systems Approach", was the production of optimal surgery schedules at hospitals. When a patient enters a hospital for an elective operation, it is imperative that a bed is available for them to go into after surgery. However, sometimes all beds in the ward will be occupied, meaning that the operation will have to be cancelled. Our research has shown that cancellations occur far less frequently if operations are scheduled throughout the week appropriately. As part of this, our models also need to take into account various hospital requirements, the unexpected arrival or emergency patients, and the variability in the time patients take to recover after their operations. More information can be found in my list of publications.

    Make a Large Distance Matrix:

    A distance matrixMany transportation problems in operational research, such as the travelling salesman problem and vehicle routing problem, make use of a distance matrix. This is a matrix that gives the optimal driving distance (or travel time) between all pairs of locations within a set. A small number of online services exist for producing distance matrices, such as the Google Maps Distance Matrix API, but the sizes of matrices offered are usually very limited. To these ends, here is my own program for producing large distance matrices and travel time matrices. This program is coded in C# and uses Bing Maps to produce matrices of up to 1079x1079 entries in a single sitting. Larger matrices can also be produced, though users should ensure they do not exceed the Bing daily usage limit (see the instructions within the attachment).

    Sudoku:

    Converting Sudoku into Graph ColouringSome of my work has concerned the use of metaheuristics to help solve sudoku puzzles. The C++ code used for experiments described in "Lewis, R. (2007). 'Metaheuristics can Solve Sudoku Puzzles' Journal of Heuristics, vol. 13 (4), pp. 387-401", can be downloaded here. Code for the random sudoku problem instance generator used in this research can be downloaded here.

    Regarding Sudoku, here is a very useful Sudoku to Graph Coloring Converter. This program reads in a single sudoku problem (from a text file) and converts it into the equivalent graph coloring problem. The output file appears in the DIMACS format which can then be used as the input file for any suitable graph coloring algorithm. If a solution using the correct number of colours is found, this can then be easily converted back into the Sudoku representation, giving a valid solution to the original problem.

    Travelling Salesman Problem:

    Screenshot of Excel TSP ProgramThe travelling salesman problem (TSP) asks the following: "Given a set of cities with distances between each pair, what is the shortest tour that visits each city exactly once?" This is a famous example of an NP-hard problem and it is often used to help motivate the use of heuristics and metaheuristics. To these ends, here is a simple MS Excel-based program for finding approximate solutions to randomly generated Euclidean TSPs. This program is intended to show the run-time characteristics of (1) random descent, (2) simulated annealing, (3) steepest descent, (4) tabu search, and (5) evolutionary algorithms. A number of animated charts are also included to help illustrate their various run-time characteristics.

    Visualising Sociograms:

    An example sociogramA sociogram is a graphical representation of social links between people. The example on the right shows data collected in a small classroom. Each node represents a student and arrows indicate friendships. Black arrows indicate that A likes B, and double-headed blue arrows indicate that both A and B like each other. Colours on the nodes are also used it indicate students that are popular (green), rejected (grey), controversial (orange) and neglected (pink). These classifications are based on the work of Robin Banerjee, found here.

    An easy-to-use Excel-based tool for creating sociogram visualisations can be found by following this link. When drawing the network, the program uses a variant of simulated annealing to decide where to place each node on the plane, making the network easier to interpret visually. The cost function of this algorithm seeks to ensure a suitable balance between five criteria: (a) the arrows should not be too long; (b) pairs of arrows should not cross; (c) nodes should be spread out evenly; (d) nodes should not be too close to the border; and (e) arrows should not pass too closely to other nodes. If you want to know more about this optimisation algorithm, please contact me.

    Sports Scheduling:

    Previous work has also looked at the scheduling of sports events, and in particular round-robin leagues and tournaments. Here is a link to the ten sports scheduling benchmark problem files used in the paper "Lewis, R. and J. Thompson (2011) 'On the Application of Graph Colouring Techniques in Round-Robin Sports Scheduling'. Computers and Operations Research, vol. 38(1), pp. 190-204". Source code (C++) for the round-robin to graph colouring program, which was used to generate many of the graphs in the paper, can be found here.

    Other Links:

    Want to avoid sitting next to annoying guests at a party? See state of the art combinatorial optimisation techniques in action with this Wedding Seating Planner tool available at www.weddingseatplanner.com.

    Maybe you're going on holiday and want to reduce airline baggage charges. If so, have a look at these ten tips for packing suitcases, which were produced for the GoCompare website.

    Photos
    Presenting at the Hay Festival of Literature and the Arts, 2017. Surfing a secret spot somewhere in south Wales Surfing a beachbreak near my home Surfing at Surf Snowdonia