The MIT Beer Distribution Game Revisited: Genetic Machine Learning and Managerial Behavior in a Dynamic Decision Making Experiment

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@InProceedings{GeyerSchulz96a,
  crossref =     "Herrera96",
  author =       "Andreas Geyer--Schulz",
  title =        "The {M}{I}{T} Beer Distribution Game Revisited:
                 Genetic Machine Learning and Managerial Behavior in a
                 Dynamic Decision Making Experiment",
  year =         "1996",
  pages =        "658--682",
  keywords =     "genetic algorithms, genetic programming, Experimental
                 economics, organizational learning, simulation, gaming,
                 system dynamics, fuzzy genetic programming.",
  abstract =     "The paper reports on the experiment of applying
                 genetic machine learning methods to breeding heuristic
                 for playing the MIT beer distribution game. In the MIT
                 beer distribution game a team of four subjects acts as
                 managers of a simulated industrial production and
                 distribution system with the aim of minimising total
                 inventory. The system consists of a chain of ofur
                 coupled stock management systems with uncertain demand,
                 tiem delays, feedbacks, multiple actors,
                 non-linearities and restricted information
                 availability. The complexity of the system - it is a
                 23rd order non-linear difference equation - renders
                 calculation of the optimal behaviour intractable. In
                 the experiment threee genetic machine learning methods
                 (a simple genetic algorithm, genetic programming, and
                 fuzzy genetic programming) are applied to the beer
                 distribution game. The results of the methods are
                 compared with the previously known best solution and
                 with the performance of a group of subjects which
                 actually played the game.",
  notes =        "In \cite{Herrera96}
                 http://decsai.ugr.es/~herrera/abstracts.html#c30",
}

Genetic Programming entries for Andreas Geyer-Schulz

Citations