© Norwegian Maritime Competence Center
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Complex systems in economic, engineering and natural sciences
involve the solving of many optimisation problems. Many of the
present approaches consider operations research techniques and,
especially, optimisation models and dedicated approximation methods
(heuristics, especially). Analytically tractable models are
impractical in change settings due to their limitations in modelling
important details and features of real world complex systems.
Simulation models, on the other hand, provide the flexibility to
accommodate arbitrary stochastic elements, and generally allow
modelling of all the complexities and dynamics of real world
applications without undue simplifying assumptions. However,
simulation itself is not an optimisation approach. Thus, in this
track methods and approaches of simulation and of optimisation along
with solutions (exact, approximation method as well as heuristics)
shall be linked to solve optimisation problems faster or make their
solutions better usable (under realistic conditions).
Topics should be:
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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.
Track-Chair: Professor Dr. Frank Herrmann.
Track-Co-Chairs: Professor Dr.
Michael Manitz and Marco Trost MA
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