"Reinforcement Learning-Driven NSGA-II for Integrated Production and AGV Transportation Scheduling Under Time-of-Use Electricity "
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https://ieee-dataport.org/documents/reinforcement-learning-driven-nsga-ii-integrated-production-and-agv-transportation
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"Reinforcement Learning-Driven NSGA-II for Integrated Production and AGV Transportation Scheduling Under Time-of-Use Electricity To evaluate the efficiency of the valid inequalities, the MIP model is tested under various configurations. Tailored combinations of five valid inequalities are applied for the two objectives separately. The models are evaluated without any valid inequalities, with each of the five valid inequalities applied individually, and with all five applied simultaneously. The experiments involve 27 different task-machine combinations, with the number of jobs (n) set to 4, 6, or 8, the number of machines (m) set to 3, 5, or 10, and the number of AGVs (a) set to 2, 3, or 4. For each combination, the two objectives, Tmax and TEC, are solved separately, resulting in a total of 54 trials."
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IEEE DataPort
创建时间：
2026-04-21
