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We compare the performance of three algorithms for production planning with workload-dependent lead times. These include a clearing function model using two different methods for estimating the clearing functions, and two iterative algorithms that combine linear programming and simulation models. Our experimental comparison uses a simulation model of a re-entrant bottleneck system built with attributes of a real-world semiconductor fabrication environment. We vary the bottleneck utilization, demand patterns, the mean time to failure, and the mean time to repair. Results indicate that the clearing function model performs better than the iterative algorithms on the scaled-down system considered, giving less variable production plans and higher profit values.