Mathematical Optimization of Batch Size and Changeover Scheduling for Manufacturing Productivity Enhancement in Multi-Product Production Systems

Abstract Frequent machine changeovers and inefficient batch sizing significantly reduce operational efficiency in multi-product manufacturing systems. This research introduces an integrated mathematical optimization framework for minimizing setup losses, improving machine utilization, and increasing production throughput through optimized batch scheduling and changeover sequencing. The proposed methodology combines mixed-integer linear programming (MILP), queueing analysis, and Overall Equipment Effectiveness (OEE) modeling to determine optimal production batch sizes and machine scheduling configurations. The framework incorporates demand variability, processing time distributions, setup dependencies, inventory constraints, and production sequencing rules to generate optimal operational schedules for high-mix manufacturing environments. Simulation-based validation will be performed using real manufacturing operational datasets to compare optimized scheduling policies against conventional fixed-batch production systems. Key performance metrics will include machine idle time, production yield, setup frequency, throughput rate, cycle time, and manufacturing cost efficiency. The study aims to provide a data-oriented manufacturing optimization strategy applicable to automotive, medical device, and industrial equipment manufacturing operations where production flexibility and operational efficiency are critical.