This thesis investigates how heuristic and metaheuristic scheduling strategies influence production efficiency in a capacitated injection moulding system. A synthetic 60-job dataset with realistic lot sizes, daily machine capacities and sequence-dependent setups is used to evaluate four scheduling methods: First Come First Served (FCFS), Earliest Due Date (EDD), Simulated Annealing (SA) and a Genetic Algorithm (GA). All approaches operate under identical processing-time and capacity assumptions, allowing performance differences to be attributed solely to sequencing logic.

Processing times are calculated based on machine-specific production rates (86,400 parts/day for 8-cavity machines and 345,600 parts/day for 32-cavity machines) and a fixed sequence-dependent setup duration of 0.1667 days is applied whenever the product family changes. Each method is assessed using makespan as the primary performance metric, as it directly reflects the efficiency of machine utilisation and the distribution of setup-related idle times.

The results show that GA achieves the shortest makespan at 23.43 days, followed by FCFS at 23.77 days and SA at 23.90 days. EDD performs worst with a makespan of 29.00 days due to misalignment between due dates and tool-family structure, which increases the number of sequence-dependent setups. Although the numerical differences between methods are modest, they arise solely from differences in job ordering and therefore represent meaningful efficiency gains in a controlled MedTech production environment. The expert evaluation further confirms that even small improvements in planning horizon and setup coordination can translate into practical benefits for operational robustness and delivery reliability.Overall, the study demonstrates that metaheuristic methods particularly GA offer stable and reproducible improvements over simple heuristic rules, even in settings where job characteristics are relatively uniform. The findings highlight the relevance of optimisation-based scheduling approaches for enhancing capacity utilisation and planning transparency in regulated MedTech manufacturing.