<p>Amidst increasing demands for continuous adjustment, manufacturing sectors of critical products such as medical devices are seeking better efficiency and better responsiveness. This research proposes and evaluates an integrated heuristic framework for optimising the whole assembly flow shop for a medical device. Our integrated heuristic approach incorporates knowledge-based assembly sequence planning, theoretical assembly time estimation, and refinement of the process using Lean principles. Finally, a Discrete Event Simulation (DES) model is developed to assess system performance, featuring an AHP-based Multi-Criteria Decision Making (MCDM) module as the dynamic dispatching logic in the simulation, providing real-time order prioritisation under both static and dynamic demand. Findings from this research demonstrate that layout selection is important and has led to significant performance improvements. The implementation of Lean principles also demonstrated a 30% reduction in assembly cycle time, underscoring the impact of targeted waste removal. Additionally, the simulation results demonstrated that, across all scenarios tested, the proposed rule significantly outperformed the conventional rules, achieving the lowest makespan, mean flow time, and mean tardiness. Overall, the data indicate that bringing together lean principles and MCDM-based scheduling heuristics has the potential to improve productivity within assembly lines dramatically. Although the proposed framework and case study are based on one unique model and study, they offer a data-driven and flexible approach that manufacturers across the discrete assembly sectors can implement. This research also has societal significance in describing a means to enhance the efficiency and timeliness in the production of critical medical devices, thereby reducing patient access and increasing responsiveness to healthcare systems.</p>