Simulation and Optimization

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:

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