Penerapan Metode Simpleks dan Regresi Linear Sederhana untuk Optimasi Produksi dan Peramalan Permintaan Bulanan Barang
Keywords:
Monthly demand of goods,, Linear programming formulationAbstract
Penelitian ini bertujuan untuk mengkaji secara sistematis penerapan metode simpleks dan regresi linear sederhana dalam optimasi produksi serta peramalan permintaan bulanan barang. Metode yang digunakan adalah Systematic Literature Review (SLR) dengan basis data utama Scopus. Pada tahap identifikasi awal, diperoleh 407 publikasi yang berkaitan dengan topik monthly demand of goods, peramalan permintaan, dan linear programming formulation dengan klasifikasi kuartil jurnal Q1 hingga Q4. Selanjutnya dilakukan proses penyaringan berdasarkan kriteria inklusi dan eksklusi yang telah ditetapkan, yaitu kesesuaian dengan fokus penelitian, terindeks Scopus (Q1–Q4), penggunaan regresi dan/atau pemrograman linear, serta ketersediaan teks lengkap. Hasil penyaringan awal menghasilkan 97 artikel yang dinilai relevan untuk dikaji lebih lanjut. Dari jumlah tersebut, dipilih 21 artikel utama untuk dianalisis secara mendalam terkait tujuan, metode, konteks penerapan, serta temuan utama. Berdasarkan penilaian kualitas dan relevansi terhadap fokus kajian, akhirnya diperoleh 6 artikel kunci yang dianggap paling representatif.
References
Chaturvedi, A. (2022). Handbook of Regression Analysis with Applications in R (Second Edition). Journal of the Royal Statistical Society: Series A (Statistics in Society), 185(S2), S777--S778. https://doi.org/10.1111/rssa.12943
Clarke, M., & others. (2024). Analysis and Recommendation System Based on PRISMA for Systematic Reviews. Computer Methods and Programs in Biomedicine Update. https://doi.org/10.1016/j.cmpbup.2024.100164
Design, L., Fereshtehnejad, E., Shafieezadeh, A., & Hur, J. (2022). Optimal budget allocation for bridge portfolios with element-level inspection data : a constrained integer linear programming formulation. Structure and Infrastructure Engineering, 18(6), 864–878. https://doi.org/10.1080/15732479.2021.1875489
Egharevba, A. J., & Ojekudo, N. A. (2021). Optimal Raw Materials Mix Through Linear Programming in Tehinnah Cakes and Craft. International Journal of Applied Science and Mathematical Theory, 7(2), 36–42.
Farizal, Qaradhawi, Y., Cornelis, C. I., & others. (2020). Fast Moving Product Demand Forecasting Model with Multi Linear Regression. AIP Conference Proceedings, 2227, 40028.
Forgenie, D., Singh, K., Sookhai, S., Khoiriyah, N., Suchit, C., Simbhoo, G., & Isaac, W. P. (2024). Heliyon Tree nuts demand analysis using the LA-AIDS model : A case of the Indian economy paradox. Heliyon, 10(13), e34238. https://doi.org/10.1016/j.heliyon.2024.e34238
Gill, K. S., Sharma, A., & Saxena, S. (2024). A Systematic Review on Game-Theoretic Models and Different Types of Security Requirements in Cloud Environment: Challenges and Opportunities. Archives of Computational Methods in Engineering, 31, 3857–3890. https://doi.org/10.1007/s11831-024-10095-6
Golden, B., Schrage, L., Shier, D., & Apergi, L. A. (2024). The unexpected power of linear programming: an updated collection of surprising applications. Annals of Operations Research, 343, 573–605. https://doi.org/10.1007/s10479-024-06245-5
Hossain, M., & Ameen, N. (2023). Improving Transparency and Quality in Systematic Reviews through PRISMA Guidelines. Journal of Evidence-Based Research, 12(3), 210–225. https://doi.org/10.1080/00000000.2023.112233
Ivanov, D. (2024). Demand Forecasting, Production Planning, and Inventory Control. In Introduction to Supply Chain Analytics (pp. 21–47). Springer.
Nasir, A., & Mulyono, S. (2023). Model WATaSE dalam Pengembangan Kajian Literatur
Sistematis. Jurnal Ilmiah Pendidikan Dan Penelitian Sosial, 9(2), 55–68. https://doi.org/10.33369/jipps.9.2.55-68
Ngume, L. S., Katalambula, L. K., Munyogwa, M. J., Mongi, R. J., & Lyeme, H. (2023). Formulation and nutritional properties of qualea-bird-meat-based complementary foods for children ( 6 – 23 months ) in Tanzania using a linear programming technique. 30(December 2022), 1–7.
Osuna-coutiño, J. A. D. E. J., Escobar-gómez, E. N., Medina-santiago, A., Aguilár-gonzález, A., Pérez-patricio, M., & Lopez-nava, I. H. (2025). Fuzzy Linear Programming Formulation for Time Prediction in Product Delivery. August, 176327–176344. https://doi.org/10.1109/ACCESS.2025.3617385
Pardoe, I. (2013). Applied Regression Modeling (2nd ed.). Wiley.
Punia, S., & Shankar, S. (2022). Predictive analytics for demand forecasting: A deep learning-based decision support system. Knowledge-Based Systems, 258, 109956. https://doi.org/10.1016/j.knosys.2022.109956
Sembiring, D. A. B., Jovanka, A., Lestari, A. W., Mahyudin, & Tampubolon, R. A. (2023). Implementation of Linear Program Using Simplex Method to Optimize Production Results in a Convection Shop. Journal of Mathematics and Scientific Computing with Applications, 4(2), 48–54.
Stanivuk, T., Kovačević, G., Čović, M., & Maleš, M. (2024). Application of the Simplex Method in the Production of Communication Modules. WSEAS Transactions on Business and Economics, 21, 1672–1678. https://doi.org/10.37394/23207.2024.21.136
Stinchfield, G., Khalife, N., Ammari, B. L., Morgan, J. C., Zamarripa, M., & Laird, C. D. (2025). Mixed-Integer Linear Programming Formulation with Embedded Machine Learning Surrogates for the Design of Chemical Process Families. https://doi.org/10.1021/acs.iecr.4c03913
Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3, 100026. https://doi.org/10.1016/j.sca.2023.100026
Turkmen, T., & Tseng, J. (2024). Demand Forecasting with Machine Learning.
Tuza, P. V. (2023). Modified Mixed-Integer Linear Programming Formulation Implemented in Microsoft Excel to Synthesize a Heat Exchanger Network with Multiple Utilities to Compare Process Flowsheets.
Zhou, Z., Liu, H., Dai, Y., & Qin, L. (2023). A Tent-Lévy-Based Seagull Optimization Algorithm for the Multi-UAV Collaborative Task Allocation Problem. Applied Sciences.






