Simulation

In many cases, practical problems are mathematically so complex that they cannot be solved analytically (yet). In these cases event-driven stochastic simulation are used which allow to examine basically any stochastic issues. However, complex problems sometimes can lead to very long simulation times, which restricts the usefulness of the results, especially if the simulation has to be repeated in the context of optimizing many times.

Probabilities and probability distributions

Simulation software

TU Clausthal offers a number of simulation programs via the Simulation Science Center. These are all open source programs that can be used free of charge:

Mini Simulator (Webapp)

The Mini Simulator is a web app fully implemented in Javascript that can be run in any modern browser (including tablets and smartphones). The app maps a G/G/c/K+M model, i.e. a model consisting of a queue and a operating station. Batch arrivals, batch operations, customer impatience, repeaters and forwarding can be mapped.

Mini Callcenter Simulator

Mini Callcenter Simulator essentially reproduces the same G/G/c/K+G model that the webapp contains. However, it has much more probability distributions that can be used for inter-arrival times, service times, post-processing times, waiting time tolerances and repeat distance distributions. In addition, considerably more characteristics are recorded and various export options are available for the simulation results. Furthermore, the simulation results can be directly compared to corresponding Erlang-C results and explanations can be displayed why deviations occur at which points.

Mini Callcenter Simulator requires a Java runtime environment and has been published as open source.

Callcenter Simulator

Callcenter Simulator is designed to map real call center systems consisting of several sub-call centers, different caller groups, different agent groups (with different skill levels and different shift plans), complex assignment rules, etc. It can be used directly for staff requirements planning and for the analysis of possible control strategies in large call center networks. In addition to pure simulation, the program also provides functions for automatic optimization of the number of agents.

Callcenter Simulator requires a Java runtime environment and has been published as open source.

Warteschlangensimulator

Warteschlangensimulator allows the simulation of any complex queueing network. The models are defined in Warteschlangensimulator in the form of flowcharts. Optionally, an animation can be displayed during the simulation of the models to illustrate the movement of the customers through the system. For the automated examination of different models, parameter series can be created automatically and an optimizer is also available. Furthermore, external data sources can be connected directly during the simulation of models and (partial) results can also be transferred directly to external programs (e.g. databases).

Warteschlangensimulator requires a Java runtime environment and was published as open source.

Literature

 

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