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EasySched: a multi-agent architecture for the predictive and reactive scheduling of Industry 4.0 production systems based on the available renewable energy.

, , and . CoRR, (2019)

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Using IoT in breakdown tolerance: PSO solving FJSP., , , , , and . IDT, page 19-24. IEEE, (2016)EasySched: a multi-agent architecture for the predictive and reactive scheduling of Industry 4.0 production systems based on the available renewable energy., , and . CoRR, (2019)Dynamic Clustering of PI-Hubs Based on Forecasting Demand in Physical Internet Context., , , , and . SOHOMA, volume 853 of Studies in Computational Intelligence, page 27-39. Springer, (2019)Cooperation Between Smart Manufacturing Scheduling Systems and Energy Providers: A Multi-agent Perspective., , , , and . SOHOMA, volume 803 of Studies in Computational Intelligence, page 197-210. Springer, (2018)Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand., , , , and . Int. J. Prod. Res., 59 (24): 7491-7515 (2021)Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns., , , , , and . Computers & Industrial Engineering, (2017)A New Rescheduling Heuristic for Flexible Job Shop Problem with Machine Disruption., , , , and . SOHOMA, volume 762 of Studies in Computational Intelligence, page 461-476. Springer, (2017)