Construction of a refined people management model for SMEs based on discrete regression algorithm

[1] Ge, Y., & Xu, B. (2016). Dynamic Staffing and Rescheduling in Software Project Management: A Hybrid Approach. PLOS ONE, 11(6). Search in Google Scholar

[2] Shen, X., Minku, L. L., Bahsoon, R., et al. (2016). Dynamic Software Project Scheduling through a Proactive-Rescheduling Method. IEEE Transactions on Software Engineering, 42(7), 658-686. Search in Google Scholar

[3] Shen, X., Minku, L. L., Marturi, N., et al. (2018). A Q-learning-based memetic algorithm for multi-objective dynamic software project scheduling. Information Sciences, 1-29. Search in Google Scholar

[4] Luna, F., Gonzalezalvarez, D. L., Chicano, F., et al. (2014). The software project scheduling problem: A scalability analysis of multi-objective metaheuristics. Applied Soft Computing, 136-148. Search in Google Scholar

[5] Abeyrathna, K. D., & Jeenanunta, C. (2019). Hybrid Particle Swarm Optimization With Genetic Algorithm to Train Artificial Neural Networks for Short-Term Load Forecasting. International Journal of Swarm Intelligence Research, 10(1), 1-14. Search in Google Scholar

[6] Kaganski, S. (2017). Implementation of Key Performance Indicators Selection Model as Part of the Enterprise Analysis Model. Procedia CIRP, 03(03), 283-288. Search in Google Scholar

[7] Amzat, I. H. (2017). Key Performance Indicators for excellent teachers in Malaysis. International Journal of Productivity and Performance Management, 03(03), 298-319. Search in Google Scholar

[8] Shishido, H. Y., Estrella, J. C., Toledo, C. F. M., et al. (2018). Genetic-based algorithms applied to a workflow scheduling algorithm with security and deadline constraints in clouds. Computers & Electrical Engineering, 69, 378-394. Search in Google Scholar

[9] Li, Z., Ge, J., Hu, H., et al. (2015). Cost and energy aware scheduling algorithm for scientific workflows with deadline constraint in clouds. IEEE Transactions on Services Computing, 11(4), 713–726. Search in Google Scholar

[10] Jafarnejad Ghomi, E., Masoud Rahmani, A., Nasih Qader, N. (2017). Load-balancing algorithms in cloud computing: a survey. Journal of Network and Computer Applications, 88, 50–71. Search in Google Scholar

[11] Regalado, F. F. J., Esenarro, D., Reátegui, M. D., et al. (2021). Model based on balanced scorecard applied to the strategic plan of a Peruvian public entity. 3c Empresa: investigación y pensamiento crítico, 10(4), 127-147. Search in Google Scholar

[12] Maqache, N., Swart, A. J. (2021). Remotely measuring and controlling specific parameters of a PV module via an RF link. 3c Tecnología: glosas de innovación aplicadas a la pyme, 10(4), 103-129. Search in Google Scholar

[13] Qu, C., Calheiros, R. N., Buyya, R. (2018). Auto-scaling web applications in clouds: a taxonomy and survey. ACM Computing Surveys, 51(4), 73:1-73:33. Search in Google Scholar

[14] Netto, H. V., Lung, L. C., Correia, M., et al. (2017). State machine replication in containers managed by Kubernetes. Journal of Systems Architecture, 73, 53–59. Search in Google Scholar

[15] Waibel, P., Hochreiner, C., Schulte, S., Koschmider, A., & Mendling, J. (2019). ViePEP-C: A container-based elastic process platform. IEEE Transactions on Cloud Computing, 9(4), 1657-1674. Search in Google Scholar

[16] Nguyen, T.-T., Yeom, Y.-J., Kim, T., et al. (2020). Horizontal pod autoscaling in Kubernetes for elastic container orchestration. Sensors, 20(16), 4621. Search in Google Scholar