MODEL OPTIMASI BERBASIS LINEAR PROGRAMMING DAN TWO-STAGE STOCHASTIC PROGRAMMING UNTUK ALOKASI SUMBER DAYA DARURAT DI KAWASAN INDUSTRI
Abstract
Industrial zones play a critical role in economic growth but are highly vulnerable to disasters that can disrupt operations and emergency energy supply. This study develops an optimization model for emergency resource allocation in the Maspion Industrial Area, Gresik, by integrating Linear Programming (LP) and Two-Stage Stochastic Programming (SP). The LP approach is applied to optimize resource distribution under deterministic conditions, while the SP model addresses uncertainty arising from disaster scenarios. Geospatial data and road networks are analyzed using Python, combined with historical disaster data to construct probabilistic scenarios. The results show that the LP model keeps transport costs low at IDR 2.150.000. However, the SP model is safer and more reliable for disasters, with a total cost of IDR 2.815.848 (about 30.95% higher). The model is accurate, with a low error rate of 17.8%. It also speeds up distribution time by 30% compared to manual methods. This study provides a data-driven tool to help managers make better, faster decisions during emergencies.
References
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[2] Badan Pusat Statistik Kota Surabaya, “Jumlah Perusahan Industri Besar dan Sedang yang Aktif Menurut Kecamatan Di Kota Surabaya Tahun 2020", www.bps.go.id.
[3] Badan Pusat Statistik,“Statistik Industri Manufaktur Produksi”, www.bps.go.id.
[4] Badan Pusat Statistik, "E. Indonesia, T. Tumbuh, dan P. C-to-c, “Pertumbuhan Ekonomi Indonesia Triwulan IV-2024”, No. 17, 2025, www.bps.go.id.
[5] J. Pemikiran dan S. Volume, “Determinasi Kapitalisme Industri dalam Politik Penataan Ruang Perkotaan di Kabupaten Gresik,” vol. 4, no. 1, hal. 25–41, 2017, doi: https://doi.org/10.22146/jps.v4i1.23624.
[6] I. M. Siregar, A. Larasati, dan A. Muid, “Analysis of the Implementation of Hazard Identification, Risk Assessment and Risk Control (HIRARC) in the Work Environment Against Work Accidents (Case Study of PT XYZ,” J. Ilm. Tek. Ind., vol. 6869, 2023, doi: https://doi.org/10.23917/jiti.v22i2.22810.
[7] Badan Nasional Penanggulangan Bencana, “Data Bencana Indonesia 2024,” https://www.bnpb.go.id/buku/buku-data-bencana-indonesia-tahun-2024.
[8] X. Yang, W. Cao, K. Wang, H. Yin, J. Wu, dan L. Wu, “Integrated scheduling of truck and drone fleets for cargo transportation in post-disaster relief : A two-stage stochastic optimization approach,” vol. 196, no. December 2024, 2025, Doi: https://doi.org/10.1016/j.tre.2025.104015.
[9] K. Eberhardt, F. Schultmann, P. Fuchß, F. K. Kaiser, dan S. Rosenberg, “International Journal of Production Economics Stochastic network optimization for strategic resource pre-positioning and allocation,” Int. J. Prod. Econ., vol. 287, no. May, hal. 109679, 2025, doi: https://doi.org/10.1016/j.ijpe.2025.109679.
[10] F. Ansari, A. Bozorgi-amiri, dan H. Shakibaei, “Engineering Applications of Artificial Intelligence A data-driven multi-stage stochastic optimization for sustainable humanitarian supply chain using machine learning algorithms,” vol. 161, no. September, 2025, https://doi.org/10.1016/j.engappai.2025.112133.
[11] I. Gago-carro, U. Aldasoro, J. Ceberio, dan M. Merino, “A stochastic programming model for ambulance ( re ) location – allocation under equitable coverage and multi-interval response time,” Expert Syst. Appl., vol. 249, no. PC, hal. 123665, 2024, https://doi.org/10.1016/j.eswa.2024.123665.
[12] A. Aghsami, S. Sharififar, N. Markazi, dan E. Hazrati, “A bi-objective mixed-integer non-linear programming model with Grasshopper Optimization Algorithm for military-based humanitarian supply chains,” Decis. Anal. J., vol. 10, no. February, hal. 100409, 2024, doi: https://doi.org/10.1016/j.dajour.2024.100409.
[13] B. Su, A. D. Ariano, S. Su, dan Z. Wang, “A risk-averse two-stage stochastic programming approach for backup rolling stock allocation and metro train rescheduling under uncertain disturbances,” Transp. Res. Part B, vol. 196, no. April, hal. 103233, 2025, doi; https://doi.org/10.1016/j.trb.2025.10323.
[14] X. Liu, M. Tian, Z. Dong, L. Guo, Y. Zhou, dan Y. Wang, “Multi-scenario robust stochastic programming based distributed energy resources allocation in distribution networks : Balancing economic efficiency and resilience,” vol. 266, no. September 2025, 2026, doi: https://doi.org/10.1016/j.ress.2025.111749.
[15] J. Du dan P. Wu, “Stochastic dynamic programming model for liner international scheduling optimization under ECA policies,” Mar. Pollut. Bull., vol. 222, no. P3, hal. 118766, 2025, doi: https://doi.org/10.1016/j.marpolbul.2025.118766.
[16] Y. Tu, Y. Lu, dan B. Lev, “Computers and Operations Research An interval two-stage robust stochastic programming under a bi-level multi-objective framework toward river basin water resources allocation,” Comput. Oper. Res., vol. 180, no. March, hal. 107045, 2025, doi: https://doi.org/10.1016/j.cor.2025.107045.
[17] H. Ji, R. Wang, C. Zhang, J. Yin, L. Ma, dan L. Yang, “Optimization of train schedule with uncertain maintenance plans in high-speed railways : A stochastic programming approach ✩,” Omega, vol. 124, no. 3, hal. 102999, 2024, doi: https://doi.org/10.1016/j.omega.2023.102999.
[18] D. Taghvaei, T. Ghods, dan M. Rabbani, “Computers & Industrial Engineering A bi-objective two-stage stochastic optimization for designing a resilient humanitarian relief chain considering hybrid contracts , public donations , and item perishability,” vol. 205, no. May, 2025, doi: https://doi.org/10.1016/j.cie.2025.111147.
[19] Z. Yuan, T. Wang, J. Tian, dan J. Zhang, “Mitigate the range anxiety : two-stage optimization for the electric vehicle routing problem with time windows and battery status uncertainty,” Transp. Res. Part E, vol. 205, no. October 2025, hal. 104510, 2026, doi: https://doi.org/10.1016/j.tre.2025.104510.
[20] R. He, L. Zhang, dan R. L. K. Tiong, “Two-stage stochastic optimization for emergency management of metro systems under uncertain storm floods” Reliab. Eng. Syst. Saf., vol. 264, no. PA, hal. 111325, 2025, doi: https://doi.org/10.1016/j.ress.2025.111325.
[21] L. Zhang, N. Yuan, J. Wang, dan J. Li, “Computers & Industrial Engineering Research on location-inventory-routing optimization of emergency logistics based on multiple reliability under uncertainty,” vol. 200, no. October 2024, 2025, https://doi.org/10.1016/j.cie.2024.11082.
[22] Y. Guan, “A comprehensive methodology combining machine learning and unified robust stochastic programming for medical supply chain viability ☆,” vol. 133, no. December 2024, 2025, doi: https://doi.org/10.1016/j.omega.2024.103264.
[23] Z. Chen, J. Jiang, J. Zhao, J. Peng, dan X. Zhang, “The bi-objective emergency system location-allocation approach based on the improved risk assessment in petrochemical industrial zone,” J. Loss Prev. Process Ind., vol. 98, hal. 105759, 2025, doi: https://doi.org/10.1016/j.jlp.2025.105759.
[24] Y. Long dan Q. Chen, “Computers & Industrial Engineering Optimizing multi-stage stochastic charging schedules for an airport bridge battery energy storage system,” Comput. Ind. Eng., vol. 211, no. October 2025, hal. 111619, 2026, https://doi.org/10.1016/j.cie.2025.111619.
[25] W. Yu, “A novel stochastic-robust optimization model for emergency supplies prepositioning under uncertain scenario probabilities,” vol. 297, no. August 2025, 2026, doi: https://doi.org/10.1016/j.eswa.2025.129380.
[26] K. Huang, W. Chen, Y. Wu, dan J. Chen, “Supply Chain Analytics A stochastic programming approach to the location of distribution centers for multinational enterprises under demand uncertainty,” vol. 11, no. October 2024, 2025, https://doi.org/10.1016/j.sca.2025.100147.
[27] Y. Liu, W. Chen, dan J. Qiu, “International Journal of Production Economics Online platform-based reverse logistics network design for mobile phones with resilience capability : Stochastic programming and Benders,” Int. J. Prod. Econ., no. October, hal. 109813, 2025, https://doi.org/10.1016/j.ijpe.2025.109813.
[28] X. Liu, M. Tian, Z. Dong, L. Guo, Y. Zhou, dan Y. Wang, “Multi-scenario robust stochastic programming based distributed energy resources allocation in distribution networks : Balancing economic efficiency and resilience,” Reliab. Eng. Syst. Saf., vol. 266, no. PB, hal. 111749, 2026, doi: https://doi.org/10.1016/j.ress.2025.111749.
[29] Z. Lin, E. Demir, X. Xu, dan G. Laporte, “An advanced hybrid approach for emergency healthcare pickup and delivery with unmanned aerial vehicles under a stochastic,” Transp. Res. Part E, vol. 204, no. 66, hal. 104395, 2025, doi: https://doi.org/10.1016/j.tre.2025.104395.
Published
2025-12-31
How to Cite
SIREGAR, Ivana Maretha et al.
MODEL OPTIMASI BERBASIS LINEAR PROGRAMMING DAN TWO-STAGE STOCHASTIC PROGRAMMING UNTUK ALOKASI SUMBER DAYA DARURAT DI KAWASAN INDUSTRI.
Jurnal Teknik Informasi dan Komputer (Tekinkom), [S.l.], v. 8, n. 2, p. 513-524, dec. 2025.
ISSN 2621-3079.
Available at: <https://jurnal.murnisadar.ac.id/index.php?journal=Tekinkom&page=article&op=view&path%5B%5D=2271>. Date accessed: 21 apr. 2026.
doi: https://doi.org/10.37600/tekinkom.v8i2.2271.
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