Prof. Dr. Alexander Schiendorfer

                                Room: 
                            K205
                        
                    
            
                    Subject Area: 
                    AI-based Optimization in Automotive Production
                
            
                        Faculty: 
                    
                        Fakultät WI
                        
                    
                
            
Research
Possible applications of combinatorial optimization and machine learning in production & logistics, especially with the requirements of the automotive industry (assembly-intensive, variant-rich production, complex supply chains)
- Constraint programming, mathematical optimization (e.g. MiniZinc, Google OR-Tools, Gurobi, CPLEX)
 - Machine learning (uncertainty-aware deep learning, reinforcement learning, graph neural networks)
 
Ongoing research projects:
Vita
- Since March 2021 Research professor für AI-based Optimization in Automotive Production, Technische
Hochschule Ingolstadt - 2018-2020           Senior researcher (untenured), Institut für Software & Systems Engineering,, University of
Augsburg - 2013-2018 Research associate and doctorate in computer science, Institut für Software & Systems Engineering,, University of Augsburg
 - 2011-2013 M.Sc. Software Engineering (University of Augsburg, TUM, LMU - Elite graduate program)
 - 2011 Research internship at Siemens Corporate Research (Princeton, USA)
 - 2011 B.Sc. Software Engineering (Hagenberg, Austria)
 
Full CV available upon request
( https://www.linkedin.com/in/alexander-schiendorfer/ or https://twitter.com/schienal ) 
Student research assistants
- Erik Hass
 - Anand Balaji .
 
Former members
- Nitin Augustine
 - Zübeyir Oflaz
 - Celeste Groux
 - Sidhant Bhavnani
 
Publications
2025
        LODES, Lukas, Erik HASS, Kristina DACHTLER und Alexander SCHIENDORFER, 2025. SmartManPy – Open Source Synthetic Manufacturing Data. Procedia Computer Science, 2025(253), 1830-1839. ISSN 1877-0509. Available at: https://doi.org/10.1016/j.procs.2025.01.245
                            


