Evolutionary Scheduling

Evolutionary scheduling is a vital research domain at the interface of two important sciences - artificial intelligence and operational research. Scheduling problems are generally complex, large scale, constrained, and multi-objective in nature, and classical operational research techniques are often inadequate at solving them effectively. With the advent of computation intelligence, there is renewed interest in solving scheduling problems using evolutionary computational techniques. These techniques, which include genetic algorithms, genetic programming, evolutionary strategies, memetic algorithms, particle swarm optimization, ant colony systems, etc, are derived from biologically inspired concepts and are well-suited to solve scheduling problems since they are highly scalable and flexible in terms of handling constraints and multiple objectives. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling, and demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems. The intended readers of this book are engineers, researchers, practitioners, senior undergraduates, and graduate students who are interested in the field of evolutionary scheduling.

• Computational Intelligence
• Evolution
• Evolutionary Algorithms
• Fuzzy
• Operations Research
• algorithm
• artificial intelligence
• evolutionary algorithm
• evolutionary strategies
• genetic algorithms
• heuristics
• intelligence
• metaheuristic
• optimization
• programming

• Modeling Optimisation Scheduling and Intelligent Control (MOSAIC), Research Centre, Department of Computing, University of Bradford, Bradford, UK

  Keshav P. Dahal, Peter I. Cowling

• Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

  Kay Chen Tan

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