Today's globalization market drives industries toward increased expectations on lean production. These expectations have put industries under pressure to become more agile under highly dynamic market and manufacturing conditions in the high-mix low-volume manufacturing systems. Dynamic production scheduling is a key factor in fulfilling the customer's expectation. It becomes more critical due to dynamics and uncertainty in the manufacturing systems. This research addresses the uncertainty consideration of machine and labor for dynamic production scheduling. Fuzzy based system is used to capture the labor and machine uncertainty and implemented in simulation environment. Based on the variability from the simulation environment, a genetic algorithm based optimization tool is developed for dynamic production scheduling. The proposed method is validated with real-world applications.
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