Abstract
Traditional scheduling approaches often rely on simplified process representations to reduce computational complexity, failing to capture the real-world dynamics where tasks often overlap, and their timing depends on finer operational steps. To address these limitations, this paper proposes a novel process representation that breaks down production tasks into smaller, more primitive steps called operations. Unlike traditional methods, this approach provides a more granular depiction of task timing and resource dependencies. Operations can define the start or end of other tasks, utilize shared resources, and incorporate recipe constraints that mandate task sequencing. The proposed representation is utilized to develop two MILP models to address the makespan and the cycle time minimization problems. Finally, the efficiency and practical applicability of the developed models are showcased with a help of a case study from the pharmaceutical industry.