A Two-Phase Grammar-Based Genetic Algorithm for a Workshop Scheduling Problem

  • Andreas Geyer-Schulz
  • Anke Thede
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


In this contribution we present a two-phase grammar-based genetic algorithm that we use to solve the problem of workshop scheduling in an educational environment which respects partial preferences of participants. The solution respects constraints on workshop capacities and allows for different schedule types. We approach this problem by defining a grammar which defines a language for expressing the restrictions on workshops and participants. A word of this formal language represents a solution which by definition of the language is always feasible. For each feasible schedule the fitness is the result of optimizing the group’s social welfare function which is defined as the sum of the individual utility functions as expressed by the partial preferences. This optimization is achieved with an order based genetic algorithm which assigns to each participant his personal schedule.


Genetic Algorithm Knapsack Problem Feasible Schedule Terminal Symbol Partial Preference 
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 2005

Authors and Affiliations

  • Andreas Geyer-Schulz
    • 1
  • Anke Thede
    • 1
  1. 1.Schroff-Stiftungslehrstuhl Informationsdienste und elektronische Märkte, Institut fur Informationswirtschaft und -managementUniversität Karlsruhe (TH)KarlsruheGermany

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