Complex systems in economic, engineering and natural sciences involve the solving of many optimization problems. Many of the present approaches consider operations research optimization models. 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 optimization approach. Thus, in this track methods and approaches of simulation and of the solution of optimization problems shall be linked to solve optimization problems faster or make their solutions better usable (under realistic conditions).

Topics should be

• Applications of operations research optimization on business processes in general as well as applications in economic, engineering and natural sciences.

• Analysis and modelling of complex systems.

• Optimization procedures and optimization potentials of complex systems.

• Combinatorial optimization and integer programming tools to handle complex systems

• Procedures of discrete event and continuous time simulation.

• (Simulation-based) heuristic and algorithmic procedures for efficiently solving complex problems.

• Simulation Optimization methods.

• Simulation-based hybrid optimization techniques.

• Utilization of simulation to make optimization 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 optimization: their more effective design for operating under realistic conditions, especially concerning shorter runtimes, as well as their architecture.