Use of rules and preferences for schedule builders in genetic algorithms for production scheduling

  • K. J. Shaw
  • P. J. Fleming
Progress in Evolutionary Scheduling
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1305)


Genetic algorithms (GAs) for problems such as the optimisation of production schedules require large amounts of complex accurate problem information to be included accurately, if optimisation is to be effective. One method of including problem information is the use of an encoding stage, such as a schedule builder, to supplement basic information contained within the chromosomes with data relevant to the manufacturing environment.

The problems of such a representation are explored, when modelling factory decisions , including the use of heuristic rules and preferences. Five schedule builder methods are implemented in the context of a real-life manufacturing example, to compare their effectiveness in improving the genetic algorithm optimisation performance.


Genetic Algorithm Schedule Problem Production Schedule Manufacturing Environment Problem Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • K. J. Shaw
    • 1
  • P. J. Fleming
    • 1
  1. 1.Department of Automatic Control & Systems EngineeringThe University of Sheffield,SheffieldUK

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