Rock aggregate demand forecasting using long short-term memory optimized by particle swarm optimization

Syarifuddin Ishak; Fitriah Fajar Maghfirah; Moch Abdillah Nafis; Haruna Lufni

doi:10.58524/jasme.v6i3.1570

Authors

Syarifuddin Ishak Universitas Tadulako, Indonesia
Fitriah Fajar Maghfirah Universitas Tadulako, Indonesia
Moch Abdillah Nafis Institut Teknologi Sepuluh Nopember, Indonesia
Haruna Lufni Institut Teknologi Sepuluh Nopember, Indonesia

DOI:

https://doi.org/10.58524/jasme.v6i3.1570

Keywords:

Computational Forecasting, Long Short-Term Memory, Particle Swarm Optimization, Rock Aggregate Demand, Time Series Forecasting

Abstract

Background: Accurate rock aggregate demand forecasting is essential for logistics planning and fleet allocation, yet highly fluctuating demand, missing observations, and non-linear temporal patterns can challenge conventional forecasting approaches.

Aim: This study aimed to evaluate a computational forecasting approach using Long Short-Term Memory optimized by Particle Swarm Optimization (LSTM-PSO) and compare its predictive performance with Croston Syntetos-Boylan Approximation (SBA) and Auto-ARIMA.

Method: Monthly rock aggregate demand from PT. Hasal Logam Utama covering January 2017–December 2021 was analyzed. Seven missing observations were treated using seasonal imputation and linear interpolation, and the resulting 60-month series was divided into 54 training and six testing observations. PSO optimized five LSTM hyperparameters, while model performance was assessed using sMAPE, MASE, RMSSE, and Forecast Accuracy (FA).

Results: LSTM-PSO achieved an sMAPE of 11.83%, MASE of 0.1552, RMSSE of 0.1139, and FA of 88.12%, compared with Croston SBA (sMAPE=17.71%; FA=82.93%) and Auto-ARIMA (sMAPE=16.86%; FA=82.15%).

Conclusion: LSTM-PSO demonstrated promising forecasting performance for the examined rock aggregate demand series, although validation using longer datasets and additional forecasting periods is required to establish broader generalizability.

References

Bacanin, N., Simic, V., Zivkovic, M., Alrasheedi, M., & Petrovic, A. (2023). Cloud computing load prediction by decomposition reinforced attention long short-term memory network optimized by modified particle swarm optimization algorithm. Annals of Operations Research, 1-34. https://doi.org/10.1007/s10479-023-05745-0

Badjan, A., Rashed, G. I., Bahageel, A. O. M., Gony, H. A. I., Shaheen, H. I., & Tuaimah, F. M. (2024). Efficient grid management: Smart forecasting of short-term power load using PSO-LSTM. Engineering Research Express, 6(3), Article 035364. https://doi.org/10.1088/2631-8695/ad7ad8

Chandriah, K. K., & Naraganahalli, R. V. (2021). RNN/LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting. Multimedia Tools and Applications, 80(17), 26145–26159. https://doi.org/10.1007/s11042-021-10913-0

Croston, J. D. (1972). Forecasting and stock control for intermittent demands. Operational Research Quarterly, 23(3), 289–303. https://doi.org/10.1057/jors.1972.50

Falatouri, T., Darbanian, F., Brandtner, P., & Udokwu, C. (2022). Predictive analytics for demand forecasting: A comparison of SARIMA and LSTM in retail SCM. Procedia Computer Science, 200, 993–1003. https://doi.org/10.1016/j.procs.2022.01.298

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

Husna, A., Amin, S. H., & Shah, B. (2021). Demand forecasting in supply chain management using different deep learning methods. In A. Taghipour (Ed.), Demand forecasting and order planning in supply chains and humanitarian logistics (pp. 140–170). IGI Global. https://doi.org/10.4018/978-1-7998-3805-0.ch005

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and practice (3rd ed.). OTexts.

Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(1), 1–22. https://doi.org/10.18637/jss.v027.i03

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679–688. https://doi.org/10.1016/j.ijforecast.2006.03.001

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the 1995 IEEE International Conference on Neural Networks (pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968

Kılıç, D. K., Nielsen, P., & Thibbotuwawa, A. (2024). Intraday electricity price forecasting via LSTM and trading strategy for the power market: A case study of the West Denmark DK1 grid region. Energies, 17(12), Article 2909. https://doi.org/10.3390/en17122909

Kolassa, S., & Schütz, W. (2007). Advantages of the MAD/mean ratio over the MAPE. Foresight: The International Journal of Applied Forecasting, 6, 40–43.

Liu, Q., He, P., Peng, S., Wang, T., & Ma, J. (2024). A survey of data-driven construction materials price forecasting. Buildings, 14(10), Article 3156. https://doi.org/10.3390/buildings14103156

Nabil, O. M., Afia, N. H., & Ismail, T. (2025). Intermittent demand forecasting for spare parts using artificial neural networks and deep learning: Literature review. JES Journal of Engineering Sciences, 53(6), 187–210. https://doi.org/10.21608/jesaun.2025.386170.1518

Nussipova, F., Rysbekov, S., Abdiakhmetova, Z., & Kartbayev, A. (2024). Optimizing loss functions for improved energy demand prediction in smart power grids. International Journal of Electrical and Computer Engineering (IJECE), 14(3), 3415–3426. https://doi.org/10.11591/ijece.v14i3.pp3415-3426

Omar, H., Klibi, W., Babai, M. Z., & Ducq, Y. (2023). Basket data-driven approach for omnichannel demand forecasting. International Journal of Production Economics, 257, Article 108748. https://doi.org/10.1016/j.ijpe.2022.108748

Sarlo, R., Fernandes, C., & Borenstein, D. (2023). Lumpy and intermittent retail demand forecasts with score-driven models. European Journal of Operational Research, 307(3), 1146–1160. https://doi.org/10.1016/j.ejor.2022.10.006

Svetunkov, I., & Boylan, J. E. (2023). iETS: State space model for intermittent demand forecasting. International Journal of Production Economics, 265, Article 109013. https://doi.org/10.1016/j.ijpe.2023.109013

Syntetos, A. A., & Boylan, J. E. (2005). The accuracy of intermittent demand estimates. International Journal of Forecasting, 21(2), 303–314. https://doi.org/10.1016/j.ijforecast.2004.10.001

Taslim, D. G., & Murwantara, I. M. (2024). Comparative analysis of ARIMA and LSTM for predicting fluctuating time series data. Bulletin of Electrical Engineering and Informatics, 13(3), 1943–1951. https://doi.org/10.11591/eei.v13i3.6034

Tian, X., Wang, H., & Erjiang, E. (2021). Forecasting intermittent demand for inventory management by retailers: A new approach. Journal of Retailing and Consumer Services, 62, Article 102662. https://doi.org/10.1016/j.jretconser.2021.102662

Türkmen, A. C., Januschowski, T., Wang, Y., & Cemgil, A. T. (2021). Forecasting intermittent and sparse time series: A unified probabilistic framework via deep renewal processes. PLOS ONE, 16, 1–26. https://doi.org/10.1371/journal.pone.0259764

Wang, S., Kang, Y., & Petropoulos, F. (2024). Combining probabilistic forecasts of intermittent demand. European Journal of Operational Research, 315(3), 1038–1048. https://doi.org/10.1016/j.ejor.2024.01.032

Yang, C.-L., Yilma, A. A., Sutrisno, H., Woldegiorgis, B. H., & Nguyen, T. P. Q. (2023). LSTM-based framework with metaheuristic optimizer for manufacturing process monitoring. Alexandria Engineering Journal, 83, 43–52. https://doi.org/10.1016/j.aej.2023.10.006

Zeng, X., Liang, C., Yang, Q., Wang, F., & Cai, J. (2025). Enhancing stock index prediction: A hybrid LSTM-PSO model for improved forecasting accuracy. PLOS ONE, 20(1), Article e0310296. https://doi.org/10.1371/journal.pone.0310296

Zhang, G. P., Xia, Y., & Xie, M. (2024). Intermittent demand forecasting with transformer neural networks. Annals of Operations Research, 339(1), 1051–1072. https://doi.org/10.1007/s10479-023-05447-7