Professorship for Modelling and Simulation of Technical Systems and Processes
Organisational unit: Professoship
Main research areas
By combining methods from the fields of information technology and operations research, production processes can be designed to be more efficient. The application of algorithms can be developed and tested in our own laboratory through the use of demonstrators.
We can simulate sequence planning and the optimisation of set-up times, as well as maintenance plans or resource allocation. The use of autonomous robots and the development of efficient planning strategies for vehicles can also be evaluated through simulations. Parameter studies and sensitivity analyses are also possible thanks to a range of interfaces.
Machine learning methods such as Gaussian processes & neural networks can predict figures based on system utilisation. Among other things, this enables the dynamic selection of control rules. What’s more, this also enables the evaluation of cause-effect relationships within processes, as well as an evaluation of the correlations between (input) parameters and their effects on the process.
Some examples of typical problems include optimising the installation and maintenance of wind turbines, optimising how high-priority tasks are dealt with in production operations, optimising intralogistics using the example of goods provision in the retail industry, dynamic rule selection in sequence planning and much more.
Most downloaded publications
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132
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Reducing mean tardiness in a flexible job shop containing AGVs with optimized combinations of sequencing and routing rules
Research output: Journal contributions › Conference article in journal › Research › peer-review
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117
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Dynamic priority based dispatching of AGVs in flexible job shops
Research output: Journal contributions › Conference article in journal › Research › peer-review
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95
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Optimal scheduling of AGVs in a reentrant blocking job-shop
Research output: Journal contributions › Conference article in journal › Research › peer-review
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94
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Closed-loop control of product geometry by using an artificial neural network in incremental sheet forming with active medium
Research output: Journal contributions › Journal articles › Research › peer-review
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88
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Anomaly detection in formed sheet metals using convolutional autoencoders
Research output: Journal contributions › Conference article in journal › Research › peer-review