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Applications of operations research
optimisation on business processes in general as well as
applications in economic, engineering and natural sciences.
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Analysis and modelling of complex systems.
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Analysis and modelling the process of control
systems design.
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Optimisation procedures and optimisation
potentials of complex systems.
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Combinatorial optimisation and integer
programming tools to handle complex systems.
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Procedures of discrete event and continuous
time simulation.
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(Simulation-based) heuristic and algorithmic
procedures (as genetic algorithms) for efficiently solving
complex problems.
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Optimisation Models for production planning
and control, for operations and business processes, for
technological devices, for logistics and so on.
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Simulation Optimisation methods.
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Simulation-based hybrid optimisation
techniques.
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Utilisation of simulation to make
optimisation problems and their (feasible) solutions usable
under industrial conditions.
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Proper handling of uncertainty and the
attainment of robust solutions.
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Methods of calibration, validation and
verification of models (under realistic conditions).
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Tools for simulation and optimisation: their
more effective design for operating under realistic conditions,
especially concerning shorter runtimes, as well as their
architecture.
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Simulation in the areas of production
planning and control, logistics, transportation, supply chain
management, and processes.
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Simulation and optimisation models with
consideration of sustainable aspects (including the economical,
ecological and social dimension).
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Simulation of continuous-time / discrete-time
/ hybrid systems for control purposes.
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Simulation of control, e.g., adaptive /
robust / predictive / nonlinear / fuzzy control.