Practical optimization problems typically involve several criteria that are
conflicting (e.g. cost, quality and environmental effects) and thus can not be
optimized simultaneously. Interactive multiobjective optimization methods have
succesfully been used in solving many such practical applications where the aim
is to find a most preferred compromise between the conflicting criteria. The
main idea is to search for a best compromise solution through an iterative
solution process where the roles of a decision maker, DM, (or a designer)
providing preference information and the optimization system alternate. This
interaction enables both the DM learning about the interdependencies between the
conflicting evaluation criteria and utilizing the experiences of the DM in
guiding the search towards interesting and preferred regions. In addition, with
this approach we can avoid computing such compromises that are not interesting
to the DM, which can lead to significant savings on computing time in the cases
of computationally costly problems.
In this presentation, applications of interactive multiobjective optimization in
the field of chemical process industry are described including their practical
challenges. Applications are related to paper making, chemical separation
processes and wastewater treatment plant design. These problems have been solved
with the IND-NIMBUS multiobjective optimization framework developed at the
University of Jyväskylä. IND-NIMBUS can be connected to different modelling and
simulation tools describing the objectives of the phenomena to be optimized.
From the optimization point of view, IND-NIMBUS contains different types of
interactive multiobjective optimization methods that can be used for problems
having different challenges (e.g. computational cost vs. accuracy, non-
convexity, how the DM wants to express preferences). In addition, IND-NIMBUS has
tools for visualizing and storing the compromise solutions computed.

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