May 31^st - June 3^rd, 2016
Regensburg, Germany

Simulation and Optimization (SIMO)

ECMS papers are listed in DBLP, SCOPUS, ISI, INSPEC and DOI

© copyright OTH Regensburg

© copyright Frank Herrmann

© copyright Frank Herrmann

© copyright Frank Herrmann

© copyright Frank Herrmann

© copyright Frank Herrmann

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

• 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

• Optimisation procedures and optimisation 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

• Optimisation Models for Operations and Business Processes, for Technological Devices and for Processes and in Finance, Economics, Logistics as well as Social Sciences

• Simulation Optimisation methods

• Simulation-based hybrid optimisation techniques

• Utilisation 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 optimisation: their more effective design for operating under realistic conditions, especially concerning shorter runtimes, as well as their architecture

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