IEEE_CIS

Evolutionary Computation Technical Committee

 IEEE

Task Force on Evolutionary Scheduling and Combinatorial Optimization

Previously

Task Force on Evolutionary Scheduling and Timetabling (2007-2013)

Dr Frank Neumann has been nominated as the new task force chair from Jan 2013. For more details refer to Evolutionary Computation Technical Committee at IEEE CIS.

Chair

Rong Qu, University of Nottingham, UK (May 2007 - Jan 2013)

Co-chairs

Ender Ozcan, University of Nottingham, UK
Andrew Parkes, University of Nottingham, UK

Members

James Bean, University of Michigan, USA
Xiaoqiang Cai, The Chinese University of Hong Kong China
Peter Fleming, University of Sheffield, UK
Jeffrey W. Herrmann, University of Maryland, USA
Raymond Kwan, University of Leeds, UK
Dirk Mattfeld, University of Braunschweig, Germany
David Montana, BBN Technologies, USA
Bryan Norman, University of Pittsburgh, USA
Kay Chen Tan, National University of Singapore, Singapore
Dario Landa Silva, University of Nottingham, UK
Edward Tsang, University of Essex, UK

Overview of the Task Force

Welcome to the Task Force on Evolutionary Scheduling and Combinatorial Optimization at IEEE Computational Intelligence Society. The aim of this working group is to serve as a forum for researchers and practitioners for promoting and carrying out research in the area of scheduling and combinatorial optimization using evolutionary computational techiques.

Evolutionary Scheduling and Combinatorial Optimization is an important research area at the interface of Artificial Intelligence and Operational Research. We are interested in both the theoretical and practical aspects related to the application of evolutionary methods to scheduling and combinatorial optimization problems.

Evolutionary methods refer to a range of computational approaches that are often inspired by processes that occur in nature. Examples of evolutionary methods are genetic algorithms, genetic programming, ant colony systems, particle swarm optimization, scatter search and path relinking, memetic algorithms, artificial immune systems, evolutionary strategies, cultural algorithms, etc. Evolutionary methods have been applied to a number of problems including optimization, search and design with considerable success. In this task force we are particularly interested in the application of evolutionary methods to tackle all types of scheduling and combinatorial optimization problems.

Scheduling and combinatorial optimization problems include a wide range of combinatorial optimization and search problems in which the task is to accommodate a set of entities such as events, items, tasks, projects, activities, people and vehicles into a pattern of time-space so that the available resources are utilized as efficiently as possible and the additional constraints are satisfied. Examples of scheduling and combinatorial optimization problems included but not limited to:

  • production scheduling
  • personnel scheduling
  • educational timetabling
  • sports timetabling
  • grid scheduling
  • transport scheduling
  • scheduling for the web
  • project scheduling
  • space allocation
  • 2D/3D strip packing
  • network routing
  • and many more

Specific objectives of the Working Group on Evolutionary Scheduling and Combinatorial Optimization include:

  • Facilitate the collaboration between researchers and practitioners in this area by means of meetings and publications in international journals.
  • Contribute to the development of original thinking in our research area.
  • Exchange experiences and knowledge, promote critical discussion, and facilitate contacts with researchers and practitioners in this research area.

Key Resources

Recent Activities and Conferences

References

  • Evolutionary Computation 1: Basic Algorithms and Operators. Baeck T, Fogel D, Michalewicz Z (eds). Institute of Physics, 2000.
  • Evolutionary Computation 2: Advanced Algorithms and Operators. Baeck T, Fogel D, Michalewicz Z (eds). Institute of Physics, 2000.
  • Evolutionary Algorithms for Solving Multi-objective Problems. Coello Coello C A, Van Veldhuizen D A, Lamont G B. Kluwer Academic Publishers, 2002.
  • Multi-objective Optimization Using Evolutionary Algorithms. Kalyanmoy D. Wiley, 2001.
  • Scheduling, Theory, Algorithms, and Systems. Pinedo M. Prentice-hall, 1995.
  • Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Leung J Y T. Chapman & Hall/CRC, Computer & Information Science Series, 2004.
  • Multicriteria Scheduling: Theory, Models and Algorithms. T'kindt V, Billaut J-C, Springer, 2002.
  • Multiobjective Scheduling by Genetic Algorithms. Bagchi T P. Kluwer Academic Publishers, 1999.
  • E. Burke and G. Kendall (eds). Introductory Tutorials in Optimisation, Decision Support and Search Methodology, Kluwer, 2004.
The Journal of Scheduling, Wiley.

Web Links

Last Update: 12 October 2012, Comments to Rong Qu