@inproceedings{Pat2021,Author = {Patrick Eschemann, Jan Elmar Krauskopf, Dr. Jürgen Sauer, Jan Stefan Zernickel},Title = {Optimizing Factory Layouts With Supervised Genetic Algorithm},Year = {2021},Pages = {73-80},Month = {10},Publisher = {Reproduct NV, Ghent, Belgium},Isbn = {978-9-492-85918-1},Booktitle = {Modelling and Simulation’2021},Url = {https://www.researchgate.net/publication/355444731_OPTIMIZING_FACTORY_LAYOUTS_WITH_SUPERVISED_GENETIC_ALGORITHM},type = {inproceedings},Abstract = {In this paper, the facility layout problem is studied using a real existing factory, with unequal-sized facilities and unequal-sized factory floor. The overall goal is to find optimized layouts to increase the productivity of the manufacturing process. To find optimal layouts a genetic algorithm is used in combination with a digital image that serves as visual interface for a supervisor. Via a 2D and 3D environment, the supervisor can apply manual mutations into the current genetic algorithm solution. The fitness of the layouts is determined by summing up material handling costs. The supervisor concept allows an unlimited number of constraints to be considered during layout generation. The solutions are compared to the actual factory layout, which was constantly improved over a period of 20 years. Through the combination of genetic algorithms and human knowledge the creation of application-oriented solutions can be reached quicker and with less effort than conventionally.}}@COMMENT{Bibtex file generated on }