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A good production schedule in a semiconductor back-end facility is critical for the on time delivery of customer orders. However, the scheduling process is usually difficult due to the wide product mix, large number of parallel machines, product family-related setups, and high weekly demand consisting of thousands of lots. In this paper, we present a new mixed-integer-linear-programming (MILP) model for the batch production scheduling of a semiconductor back-end facility with serial production stages. Computational results are provided for finding optimal solutions to small problem instances. Due to the limitation on the solvable size of the MILP formulation, a deterministic scheduling system (DSS), including an optimizer and a scheduler, is proposed to provide suboptimal solutions in a reasonable time for large real-world problem instances. Small problem instances are randomly generated to compare the performances of the optimization model and the DSS. An experimental design is utilized to understand the behavior of the DSS under different production scenarios.