In this research advanced numerical techniques and software are developed to support real-time decision making for the design and optimization of large-scale dynamic (bio)chemical processes. To achieve this aim different steps must be taken. First, improvements and extensions to current state-of-the-art methods for multi-objective optimization (MOO) are elaborated in order to obtain efficient solution strategies for optimizing dynamic (bio)chemical processes. More specically, adapted algorithmic schemes, parallelization strategies and Object-Oriented Programming have to be explored. Second, a software with a user-friendly interface has to be implemented in order to allow an eective application at the industrial level. Last, a validation of the provided techniques and software is required on industry relevant case studies to prove the possibly large contributions in, e.g., reducing costs, limiting waste, lowering energy consumption and improving safety.