| Authors: |
Andreas Geyer-Schulz
|
| Editors: |
F. Herrera
and J. L. Verdegay
|
| URL: |
http://decsai.ugr.es/~herrera/ga-sc.html |
| Tags: |
Experimental
algorithms,
dynamics,
economics,
fuzzy
gaming,
genetic
learning,
organizational
programming,
programming.
simulation,
system
|
| 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. |
@inproceedings{GeyerSchulz96a,
title = {The {M}{I}{T} Beer Distribution Game Revisited:
Genetic Machine Learning and Managerial Behavior in a
Dynamic Decision Making Experiment},
address = {Heidelberg},
author = {Andreas Geyer-Schulz},
booktitle = {Genetic Algorithms and Soft Computing},
crossref = {Herrera96},
editor = {F. Herrera and J. L. Verdegay},
month = {September},
pages = {658--682},
publisher = {Physica-Verlag},
series = {Studies in Fuzziness and Soft Computing},
url = {http://decsai.ugr.es/~herrera/ga-sc.html},
volume = {8},
year = {1996},
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.},
organisation = {Physica-Verlag}, isbn = {3-7908-0956-X}, notes = {In \cite{Herrera96}
http://decsai.ugr.es/~herrera/abstracts.html#c30},
keywords = {Experimental algorithms, dynamics, economics, fuzzy gaming, genetic learning, organizational programming, programming. simulation, system }
}
::