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

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.

• Luigi Barone, Schneider Electric, Australia (vice-chair)
• James Bean, University of Michigan, USA
• Dirk Briskorn, University of Siegen, Germany (vice-chair)
• 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
• Frank Neumann, The University of Adelaide, Australia (chair)
• Bryan Norman, University of Pittsburgh, USA
• Ender Ozcan, University of Nottingham, UK
• Rong Qu, University of Nottingham, UK
• Andrew Parkes, University of Nottingham, UK
• Kay Chen Tan, National University of Singapore, Singapore
• Dario Landa Silva, University of Nottingham, UK
• Edward Tsang, University of Essex, UK

• Special Session "Heuristic Methods for Multi-Component Optimization Problems", CEC 2014, Beijing, China
• Competition "Optimisation of Problems with Multiple Interdependent Components", CEC 2014, Beijing, China