Data-driven Optimization Techniques for Integrated Process Planning and Scheduling

• Applicants: Professor Dr. Dominik Kreß; Professor Dr. Erwin Pesch
• Subject Area: Operations Management and Computer Science for Business Administration
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 524165670

The focus of this proposal is on optimization and automation of process planning and scheduling in a make-to-order setting. Manufacturing a specific product can be done in several different ways, using a wide range of manufacturing technologies and various flexible (equipped with a set of tools) and autonomous, non-identical machines in different processing steps. Some machines need to be operated by highly qualified human operators. Decisions to be made are, among others, the selection of a process plan to produce each work piece, the machine to perform each processing step, predicting processing time, and scheduling of a heterogeneous workforce, just to name a few. Manufacturing production has to be altered in order to cope with market and technology changes. Production systems must be capable of reacting quickly to disturbances as well as to failures of machines and processes. This capability is achieved by using available technology alternatives and real-time re-calculation of production routes based on robust production plans. Data analytics and artificial intelligence will permit to retrieve relevant information from unstructured data. The generated information will define the basis for continuous improvement and learning. Our main steps towards automating production planning are data collection, model building, design learning of (exact) robust methods and finally, transfer into practice.

• DFG Programme: Research Grants
• International Connection: Israel
• International Co-Applicants: Professor Izack Cohen, Ph.D.; Professorin Dr. Shimrit Shtern