The workforce rostering for tomorrow’s industry needs to be reconsidered. The development of new types of production control mechanisms, like decentralized production control, impacts the effectivity and efficiency of workforce rostering methods, too. Simultaneously, social trends, like the growing demand of flexible working time models and labor shortages, take their influence on the rostering process. We are facing these requirements by developing a new rostering method which is appropriate for decentrally controlled production systems, the consideration of individual preferred working times independent of rigid shift systems and the simultaneous targeting of production-related performance variables. Therefore, we apply a simulation-based optimization approach which is based on a genetic algorithm
