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Abstract
Optimization has been recognized as a powerful tool in teaching and
research for a long time. In spite of its well known problem solving
capacity, some methodological obstacles have persisted over the
years. The main problem is that stochastic variables and their correlations
cannot be adequately accounted for within traditional optimization
procedures. In this paper, we develop a methodological
mix of stochastic simulation and a heuristic optimization procedure
which has become known as genetic algorithms. The simulation part
of the mix allows for the consideration of complex information
such as stochastic processes; the genetic algorithms-part ensures
that the method remains manageable in terms of required time and
resources. We demonstrate the decision support potential of the
approach by optimizing the production program of a Brandenburg
crop farm. We account for the risky environment by using existing
stochastic information: on the one hand, we model man-days which
are available in critical seasons (particularly harvesting) as triangular
distributions according to expert estimations. On the other hand,
we use empirical time series and estimate stochastic processes for
the gross margins of different activities (wheat, barley etc.). Additionally,
variant calculations are made in order to take into account
different risk attitudes of decision-makers. Model results in terms of
optimal production programs and expected total gross margins are
highly sensitive both to the risk attitudes of decision-makers and
the stochastic processes which are estimated for different activities.