Accesso libero

Research on Production Scheduling of Industrial Big Data for Internet of Things Based on Dynamic Planning Algorithm

   | 26 feb 2024
INFORMAZIONI SU QUESTO ARTICOLO

Cita

Mourtzis, D., Vlachou, E., & Milas, N. (2016). Industrial big data as a result of iot adoption in manufacturing. Procedia CIRP, 55(Complete), 290-295. Search in Google Scholar

Liao, X., Faisal, M., Qingchang, Q., Ali, A., & Khan. (2020). Evaluating the role of big data in iiot-industrial internet of things for executing ranks using the analytic network process approach. Scientific Programming, 2020. Search in Google Scholar

Yang, B., Pang, Z., Wang, S., Mo, F., & Gao, Y. (2022). A coupling optimization method of production scheduling and computation offloading for intelligent workshops with cloud-edge-terminal architecture. Journal of Manufacturing Systems. Search in Google Scholar

Atharvan, G., Krishnamoorthy, S. K. M., Dua, A., & Gupta, S. (2022). A way forward towards a technology-driven development of industry 4.0 using big data analytics in 5g-enabled iiot. International journal of communication systems(1), 35. Search in Google Scholar

Jiang, P., Ding, J. L., & Guo, Y. (2018). Application and dynamic simulation of improved genetic algorithm in production workshop scheduling. International Journal of Simulation Modelling, 17(1), 159-169. Search in Google Scholar

Lee, S., Do Chung, B., Jeon, H. W., & Chang, J. (2017). A dynamic control approach for energy-efficient production scheduling on a single machine under time-varying electricity pricing. Journal of Cleaner Production, 165(nov.1), 552-563. Search in Google Scholar

Rasti-Barzoki, M., & Hejazi, S. R. (2015). Pseudo-polynomial dynamic programming for an integrated due date assignment, resource allocation, production, and distribution scheduling model in supply chain scheduling. Applied mathematical modelling(39-12). Search in Google Scholar

Chu, Y., & You, F. (2013). Integration of scheduling and dynamic optimization of batch processes under uncertainty: two-stage stochastic programming approach and enhanced generalized benders decomposition algorithm. Industrial & Engineering Chemistry Research(52-47). Search in Google Scholar

Bautista, J., Cano, A., Companys, R., & Ribas, I. (2012). Solving the fm | block | cmax problem using bounded dynamic programming. Engineering Applications of Artificial Intelligence, 25(6), 1235-1245. Search in Google Scholar

Papadaki, K. P., & Powell, W. B. (2010). An adaptive dynamic programming algorithm for a stochastic multiproduct batch dispatch problem. Naval Research Logistics (NRL), 50. Search in Google Scholar

Zhou, B., & Wen, M. (2023). A dynamic material distribution scheduling of automotive assembly?line considering material-handling errors. Engineering Computations, 40(5), 1101-1127. Search in Google Scholar

Chang, C. Y., Li, M. H., Huang, W. C., & Lee, S. C. (2017). An optimal scheduling algorithm for maximizing throughput in wimax mesh networks. IEEE Systems Journal, 9(2), 542-555. Search in Google Scholar

Tang, X. L. (2007). Scheduling a hybrid flowshop with batch production at the last stage. Computers & Operations Research. Search in Google Scholar

Zhang, S., Tang, F., Li, X., Liu, J., & Zhang, B. (2021). A hybrid multi-objective approach for real-time flexible production scheduling and rescheduling under dynamic environment in industry 4.0 context. Computers & Operations Research, 105267. Search in Google Scholar

Xuan, H., & Tang, L. (2007). Scheduling a hybrid flowshop with batch production at the last stage. Computers & Operations Research, 34(9), 2718-2733. Search in Google Scholar

Shen, Z., Liu, M., Xu, L., & Lu, W. (2022). Coordinated scheduling of integrated transmission and distribution systems using an improved lipschitz dynamic programming approach. International Journal of Electrical Power & Energy Systems, 140, 108076-. Search in Google Scholar

Long, J., Sun, Z., Pardalos, P. M., Bai, Y., & Li, C. (2020). A robust dynamic scheduling approach based on release time series forecasting for the steelmaking-continuous casting production. Applied Soft Computing, 92, 106271. Search in Google Scholar

Pickardt, C. W., Hildebrandt, T., Branke, J., Heger, J., & Scholz-Reiter, B. (2013). Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems. International Journal of Production Economics, 145(1), 67-77. Search in Google Scholar

eISSN:
2444-8656
Lingua:
Inglese
Frequenza di pubblicazione:
Volume Open
Argomenti della rivista:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics