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Device Context Classification for Mobile Power Consumption Reduction.

, , , and . DSD, page 682-685. IEEE Computer Society, (2016)

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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)Toward Digital twin for sustainable manufacturing: A data-driven approach for energy consumption behavior model generation., , , , and . Comput. Ind., (September 2023)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)An energy-efficient scheduling and rescheduling method for production and logistics systems†., , and . Int. J. Prod. Res., 58 (11): 3263-3283 (2020)Two stage particle swarm optimization to solve the flexible job shop predictive scheduling problem considering possible machine breakdowns., , , , , and . Comput. Ind. Eng., (2017)Device Context Classification for Mobile Power Consumption Reduction., , , and . DSD, page 682-685. IEEE Computer Society, (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)Digital Twin for Decision-Support: An Insight into the Integration of Simulation Models into Digital Twin Architectures., , , and . SOHOMA, volume 1136 of Studies in Computational Intelligence, page 15-25. Springer, (2023)Toward Digital Twin for Cyber Physical Production Systems Maintenance: Observation Framework Based on Artificial Intelligence Techniques., , , and . SOHOMA, volume 952 of Studies in Computational Intelligence, page 123-134. Springer, (2020)