Journal of Physics Conference Series · 2020 · 12 citations · 10 references
Mathematical ProgrammingEngineeringReinforcement Learning (Computer Engineering)Scheduling ProblemSmart ManufacturingProcess ControlProduction SchedulingSystems EngineeringReinforcement Learning MethodSteel Production ProcessEducationReinforcement Learning (Educational Psychology)Scheduling (Production Processes)Ai-based Process OptimizationLearning ControlGenetic AlgorithmMarkov Decision ProcessOperations Research
Abstract In this paper, a reinforcement learning method is utilized to solve the steel production scheduling problem. Based on characteristics of steel production processing, the model of hybrid flow-shop scheduling problem is constructed. Then the model is attributed to a Markov Decision Process, and corresponding states, actions, reward function are put forward. When trading off the exploration and exploitation, an improved ε -greedy policy is designed. Finally, this hybrid flow-shop scheduling model based on reinforcement learning is applied to the scheduling example of steel production processing. Compared to genetic algorithm, the reinforcement learning method gets the better result.
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