Optimasi Penjadwalan Proses Produksi Berbasis Laxity dengan Algoritma ELLF untuk SmartEdu Station

Noer Fajrin, Yuliadi Erdani, Muhammad Avila Thirafi, Suharyadi Pancono, Cyndi Odilia Sumantri, Natasya Nareswari

Sari


Penjadwalan urutan eksekusi job pada mesin produksi merupakan keputusan operasional krusial yang memengaruhi throughput dan pemenuhan tenggat waktu. Di antara algoritma penjadwalan dinamis, pendekatan berbasis laxity dengan Least Laxity First (LLF) secara teoretis optimal namun mengalami laxity thrashing, yaitu peralihan konteks berlebihan ketika beberapa job memiliki nilai laxity yang sama. Penelitian ini mengimplementasikan Enhanced Least Laxity First (ELLF) sebagai perluasan LLF dengan mekanisme exclusion shield untuk menghilangkan thrashing, yang diadaptasi dari penjadwalan prosesor berskala tick mikrodetik ke skala waktu manufaktur berbasis tick detik. Algoritma diterapkan pada arsitektur IoT empat lapis berbiaya rendah menggunakan Raspberry Pi dengan tumpukan pemantauan InfluxDB dan Grafana, pada studi kasus mesin induction-forging baut. Pengujian mencakup dua skenario beban (underload dan overload) dengan delapan job periodik selama lima kali pengulangan independen. Hasil menunjukkan ELLF mencapai nol preemption pada kondisi overload (penurunan 100% dari 1,28 preemption/job pada LLF), dengan trade-off berupa kenaikan waktu tunggu sekitar 15% dan tardiness rata-rata 232 ms. Arsitektur IoT mendistribusikan telemetri penjadwal ke InfluxDB dengan latensi sub-detik tanpa mengganggu kinerja. Melalui pengujian simulasi perangkat lunak, penelitian ini membuktikan secara empiris bahwa pendekatan berbasis laxity, khususnya ELLF, efektif untuk penjadwalan produksi soft real-time pada arsitektur IoT berbiaya rendah. Sistem penjadwalan ini ditujukan untuk diintegrasikan pada SmartEdu Station.

Referensi


Calderón, D., Folgado, F. J., González, I., & Calderón, A. J. (2024). Implementation and experimental application of industrial IoT architecture using automation and IoT hardware/software. Sensors, 24(24), 8074. https://doi.org/10.3390/s24248074

Cottet, F., Delacroix, J., Kaiser, C., & Mammeri, Z. (2002). Scheduling in real-time systems. Wiley.

Daher, A. W., Rizik, A., Muselli, M., Chible, H., & Caviglia, D. D. (2021). Porting Rulex software to the Raspberry Pi for machine learning applications on the edge. Sensors, 21(19), 6526. https://doi.org/10.3390/s21196526

Deniziak, S., Płaza, M., & Arcab, Ł. (2023). Real-time communication model for IoT systems. Annals of Computer Science and Information Systems, 35, 931–936. https://doi.org/10.15439/2023F8513

Farooq, M. S., Abdullah, M., Riaz, S., Alvi, A., Rustam, F., López Flores, M. A., Castanedo Galán, J., Samad, M. A., & Ashraf, I. (2023). A survey on the role of industrial IoT in manufacturing for implementation of smart industry. Sensors, 23(21), 8958. https://doi.org/10.3390/s23218958

Fatima, Z., Tanveer, M. H., Waseemullah, Zardari, S., Naz, L. F., Khadim, H., Ahmed, N., & Tahir, M. (2022). Production plant and warehouse automation with IoT and Industry 5.0. Applied Sciences, 12(4), 2053. https://doi.org/10.3390/app12042053

Forstmayr, F., & Brunauer, F. (2021). Lazy least laxity scheduling for Linux-based programmable logic controller [Preprint]. Salzburg University of Applied Sciences. https://doi.org/10.13140/RG.2.2.28017.61281

Goyal, Y. (2024). Comparative study of microcontroller: Arduino Uno, Raspberry Pi 4, ESP 32. International Journal for Research in Applied Science and Engineering Technology, 12(7), 588–592. https://doi.org/10.22214/ijraset.2024.63598

Han, S., Paik, W., Ko, M.-C., & Park, M. (2023). A comparative study on the schedulability of the EDZL scheduling algorithm on multiprocessors. Applied Sciences, 13(18), 10131. https://doi.org/10.3390/app131810131

Hassan, A., Nahar, H., Md Shah, W., Abd-Aziz, A., Sahiran, S. A., Bahaman, N., Ahmad, M. R., Hamid, I. R. A., & Sidik, M. A. B. (2022). Performance evaluation of Raspberry Pi as an IoT edge signal processing device for a real-time flash flood forecasting system. International Journal of Advanced Computer Science and Applications, 13(10).

Hildebrandt, J., Golatowski, F., & Timmermann, D. (1999). Scheduling coprocessor for enhanced least-laxity-first scheduling in hard real-time systems. In Proceedings of the 11th Euromicro Conference on Real-Time Systems (pp. 208–215). https://doi.org/10.1109/EMRTS.1999.777467

Kermia, O. (2022). Strictly periodic first: An optimal variant of LLF for scheduling tasks in a time-critical cyber-physical system. Concurrency and Computation: Practice and Experience, 34, e5908. https://doi.org/10.1002/cpe.5908

Kocsi, B., Matonya, M. M., Pusztai, L. P., & Budai, I. (2020). Real-time decision-support system for high-mix low-volume production scheduling in Industry 4.0. Processes, 8(8), 912. https://doi.org/10.3390/pr8080912

Lin, J. A., & Fuh, C. S. (2013). 2D barcode image decoding. Mathematical Problems in Engineering, 2013, 848276. https://doi.org/10.1155/2013/848276

Manasa, R., & Mabbu, D. (2023). A comparative approach on scheduling algorithms for real-time systems. Electronics and Computer Express, 1(1), 29–35. https://doi.org/10.59535/ece.v1i1.11

Omar, H. K., Jihad, K. H., & Hussein, S. F. (2021). Comparative analysis of the essential CPU scheduling algorithms. Bulletin of Electrical Engineering and Informatics, 10(5), 2742–2750.

Rahmah, G. M., Fauziah, K., & Suroso, F. (2024). Implementasi metode Earliest Due Date (EDD) untuk penjadwalan produksi. JMEIS, 2(1), 65–72. https://doi.org/10.52330/jmeis.v2i1.267

Saha, N., Paul, P., Ji, K., & Harik, R. (2024). Performance evaluation framework of MQTT client libraries for IoT applications in manufacturing. Manufacturing Letters, 41, 1237–1245. https://doi.org/10.1016/j.mfglet.2024.09.150

Wang, C., Qiao, J., Huang, X., Song, S., Hou, H., Jiang, T., Rui, L., Wang, J., & Sun, J. (2023). Apache IoTDB: A time series database for IoT applications. Proceedings of the ACM on Management of Data, 1(2), Article 195. https://doi.org/10.1145/3589775

Wijayaningrum, V. N., & Wakhidah, R. (2023). Monitoring development board pada platform InfluxDB dan Grafana. Jurnal Informatika dan Teknologi Informasi, 20(1), 81–90.

Xie, S., & Tan, H. Z. (2021). Blur-readable two-dimensional barcode based on blur-invariant shape and geometric features. International Journal of Advanced Robotic Systems, 18(2). https://doi.org/10.1177/17298814211002613

Younis, N. K., Ahmed, M. R., Talab, A. W., & Ali, R. R. (2025). Testing real-time system algorithms performance on synthetic data: Analytical study of hard and soft system tasks. International Journal of Innovative Research and Scientific Studies, 9(5), 607–614. https://doi.org/10.55214/25768484.v9i5.6956

Zhang, Y. (2025). A dynamic scheduling method combining iterative optimization and deep reinforcement learning to solve sudden disturbance events in a flexible manufacturing process. Mathematics, 13(1), 4. https://doi.org/10.3390/math13010004

Zhao, A., Liu, Y., Zhang, Z., Wang, J., Cheng, Y., & Pan, X. (2022). Data-mining-based real-time optimization of the job shop scheduling problem. Mathematics, 10(23), 4608. https://doi.org/10.3390/math10234608

Zulfadlillah, Z., Fathani, M. A., Noval, M., & Irwana, I. (2024). Penerapan sistem Manufacturing 4.0 dengan integrasi Internet of Things (IoT) untuk optimalisasi efisiensi produksi. Juritek, 4(1), 159–164. https://doi.org/10.53625/juritek.v4i1.7234




DOI: http://dx.doi.org/10.25157/teorema.v11i2.25289

Refbacks

  • Saat ini tidak ada refbacks.


##submission.copyrightStatement##

Laman Teorema: https://jurnal.unigal.ac.id/index.php/teorema/index

Terindek: