CAPACITY OPTIMIZATION OF CNC MACHINING CENTERS IN HIGH-MIX LOW-VOLUME MANUFACTURING: AN INTEGRATED SIMULATION AND SCHEDULING APPROACH

High-mix low-volume (HMLV) manufacturing environments face persistent challenges in achieving high utilisation of CNC machining centres while simultaneously meeting delivery due-dates for diverse, low-batch-size orders. This dissertation presents an integrated framework combining discrete-event simulation (DES), analytical capacity modelling, and a modified Genetic Algorithm (GA) scheduling optimiser to maximise throughput and minimise makespan in a real-world HMLV job shop equipped with seven CNC machining centres of varying capability tiers. A capacity model based on Overall Equipment Effectiveness (OEE) and utilisation rate analysis was developed and calibrated against 18 months of production data from a precision parts manufacturer in Tashkent, Uzbekistan. Bottleneck machines were identified using the Theory of Constraints (TOC) methodology, revealing that two 5-axis centres (OEE = 61.3%) were limiting shop-floor throughput by an estimated 23%. A DES model built in Siemens Tecnomatix Plant Simulation was validated against historical key performance indicators (KPIs) within ±4.8% for throughput and ±6.1% for average flow time. The GA scheduler, operating on a 48- hour rolling horizon, reduced average job tardiness by 38.4%, increased average machining centre utilisation from 67.2% to 81.9%, and improved OEE of bottleneck centres from 61.3% to 76.8% through optimised preventive maintenance windows and setup-time reduction strategies. Annual capacity gain equivalent to 1.7 additional machine-shifts per day was demonstrated without capital investment. The framework provides a transferable decision-support tool for HMLV manufacturers seeking productivity improvement through data-driven scheduling.

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