BALIKESIR ORMAN YANGINLARIYLA MÜCADELEDE 0-1 TAM SAYILI PROGRAMLAMA ILE YANGIN SÖNDÜRME EKIPMANI PLANLAMASI

Year 2025, Volume: 9 Issue: 1, 203 - 216, 30.04.2025

Burcu Tezcan , Tamer Eren

https://doi.org/10.32328/turkjforsci.1648979

Cited By: 1

Abstract

Orman yangınları ekolojik dengenin bozulmasına, ekonomik kayıplara ve can güvenliğinin tehdit edilmesine yol açan ciddi afetler arasındadır. Bu tür afetlerle etkin bir şekilde mücadele edebilmek için yangın söndürme ekipmanlarının stratejik olarak planlanması büyük önem taşımaktadır. Bu bağlamda bu çalışmada Balıkesir ilinin orman yangınında yangın söndürme ekipman planlanması, bölgede yaşayan 32 farklı endemik tür ile hayvan türleri için önemlidir. Bu nedenle orman yangını açısından riskli bölge olan Balıkesir ili uygulama alanı olarak seçilmiştir. Balıkesir’de 8 orman yangını bölgesi seçilmiş ve 20 yangın söndürme ekipmanın planlanması yapılacaktır. 0-1 Tam Sayılı Programlama yöntemi kullanılarak orman yangınına müdahale için optimum ekipman planlama modeli geliştirilmiştir. Model yangın söndürme ekipmanlarının ulaşım sürelerini minimum yapmayı amaçlamaktadır. Her yangın bölgesine ihtiyacı kadar yangın söndürme ekipmanı tahsis edilecektir. Matematiksel model IBM ILOG CPLEX Optimizasyon programı ile çözülmüştür. Elde edilen sonuçlara göre 8 orman yangını bölgesine 68 dakika ulaşım sağlanmıştır. Bu durum modelin uygulanabilirliğini ve etkinliğini doğrulamakta olup acil durum yönetimi bağlamında operasyonel verimliliği artırma potansiyelini göstermektedir.

Keywords

Orman Yangınları , Kaynak Optimizasyonu , 0-1 Tam Sayılı Programlama , Balıkesir

OPTIMIZING FIREFIGHTING EQUIPMENT ALLOCATION IN BALIKESIR USING 0-1 INTEGER PROGRAMMING

Abstract

Forest fires have been among the most severe disasters, causing disruptions to the ecological balance, economic losses, and threats to life safety. To effectively combat such disasters, the strategic planning of firefighting equipment has been of great importance. In this context, this study focused on the planning of firefighting equipment for forest fires in Balıkesir province, which is home to 32 different endemic plant and animal species. Due to its high wildfire risk, Balıkesir was selected as the study area. In the province, eight forest fire zones were identified, and the allocation of 20 firefighting units was planned. An optimal equipment allocation model for forest fire response was developed using the 0-1 Integer Programming method. The model aimed to minimize the transportation time of firefighting equipment while ensuring that each fire zone received the necessary number of resources. The mathematical model was solved using the IBM ILOG CPLEX Optimization program. The results indicated that an optimal transportation time of 68 minutes was achieved for the eight fire zones. These findings confirmed the applicability and effectiveness of the proposed model and demonstrated its potential to enhance operational efficiency in emergency management. Moreover, the study presents a novel 0-1 integer programming model for the optimal assignment of firefighting equipment in Balıkesir province under assumptions such as fixed fire zones and equipment capacity. Future studies are planned to improve the model with operational constraints such as dynamic fire spread, personnel, and fuel limitations.

Keywords

Forest Fires , Resource Optimization , 0-1 Integer Programming , Balıkesir

Ethical Statement

This study does not require any ethics committee approval

Supporting Institution

The study received no financial support.

Thanks

We would like to thank the experts working in the General Directorate of Forestry for their valuable contributions to this article.

References

• Aalami, S., & Kattan, L. (2018). Fair dynamic resource allocation in transit-based evacuation planning. Transportation research part C: emerging technologies, 94, 307–322.
• Avci, M. G., Avci, M., Battarra, M., & Erdoğan, G. (2024). The wildfire suppression problem with multiple types of resources. European Journal of Operational Research.
• Collins, R. D., de Neufville, R., Claro, J., Oliveira, T., & Pacheco, A. P. (2013). Forest fire management to avoid unintended consequences: A case study of Portugal using system dynamics. Journal of environmental management, 130, 1–9.
• Donovan, G. H. (2006). Determining the optimal mix of federal and contract fire crews: A case study from the Pacific Northwest. Ecological modelling, 194(4), 372–378.
• Ertugrul, M. (2005). The situations of forest fires in the world and in Turkey. ZKU Bartin Faculty of Forestry Journal, 7(7), 43–50.
• Fidanboy, M., Adar, N., & Okyay, S. (2022). Development of a forest fire prediction model based on deep learning and forecast fire risk map of Turkey. Turk. J. For. Res, 9, 206–218.
• Haight, R. G., & Fried, J. S. (2007). Deploying wildland fire suppression resources with a scenario-based standard response model. INFOR: Information Systems and Operational Research, 45(1), 31–39.
• Harris, M. G., Forbes, M. A., & Taimre, T. (2023). Logic-based benders decomposition for wildfire suppression. Computers & Operations Research, 160, 106392.
• Jünger, M., Liebling, T. M., Naddef, D., Nemhauser, G. L., Pulleyblank, W. R., Reinelt, G., Rinaldi, G., & Wolsey, L. A. (2009). 50 Years of integer programming 1958-2008: From the early years to the state-of-the-art. Springer Science & Business Media.
• Kaymazlı, T. (2001). 0-1 tamsayılı programlama ile askeri tesislerin yer/proje seçimi. Marmara Universitesi (Turkey).
• MacLellan, J. I., & Martell, D. L. (1996). Basing airtankers for forest fire control in Ontario. Operations Research, 44(5), 677–686.
• Martell, D. (2007). Forest fire management. Handbook of operations research in natural resources. Springer, New York.
• Mendes, A. B., & e Alvelos, F. P. (2025). A robust optimisation approach for the placement of forest fire suppression resources. International Transactions in Operational Research, 32(3), 1312–1342.
• Minas, J., Hearne, J., & Martell, D. (2015). An integrated optimization model for fuel management and fire suppression preparedness planning. Annals of operations Research, 232, 201–215.
• Ntaimo, L., Arrubla, J. A. G., Stripling, C., Young, J., & Spencer, T. (2012). A stochastic programming standard response model for wildfire initial attack planning. Canadian Journal of Forest Research, 42(6), 987–1001.
• Rideout, D. B., Wei, Y., & Kirsch, A. (2010). Allocation of initial attack resources. WIT Transactions on Ecology and the Environment, 137, 187–195.
• Rideout, D., Wei, Y., & Kirsch, A. (2011). Optimal allocation of initial attack resources to multiple wildfire events. International journal of safety and security engineering, 1(3), 312–325.
• Rodríguez-Veiga, J., Ginzo-Villamayor, M. J., & Casas-Méndez, B. (2018). An integer linear programming model to select and temporally allocate resources for fighting forest fires. Forests, 9(10), 1–18.
• Rodríguez-Veiga, J., Gómez-Costa, I., Ginzo-Villamayor, M. J., Casas-Méndez, B., & Sáiz-Díaz, J. L. (2018). Assignment problems in wildfire suppression: Models for optimization of aerial resource logistics. Forest Science, 64(5), 504–514.
• Sari, F. (2021). Forest fire susceptibility mapping via multi-criteria decision analysis techniques for Mugla, Turkey: A comparative analysis of VIKOR and TOPSIS. Forest Ecology and Management, 480, 118644.
• Tezcan, B., Eren, T. (2025). Forest fire management and fire suppression strategies: a systematic literature review. Natural Hazards.
• Tezcan, B., Alakaş, H. M., Özcan, E., & Eren, T. (2021). Afet sonrası geçici depo yeri seçimi ve çok araçlı araç rotalama uygulaması: Kırıkkale ilinde bir uygulama. Politeknik Dergisi, 26(1), 13–27.
• Tezcan, B., & Eren, T. (2022). Orman Yangınlarına Etki Eden Faktörlerin Önceliklendirilmesi. 3rd International Disaster Management Congress.
• Tezcan, B., & Eren, T. (2023a). Orman Yangınına Sebep Olan Kriterlerin Bulanık Ortamda Değerlendirilmesi. Politeknik Dergisi, 27(2), 545–558.
• Tezcan, B., & Eren, T. (2023b). Sürdürülebilir Kalkınma için Çam İğnelerinin Enerji Üretimine Olanak Sağlayan Kriterlerin Önceliklendirilmesi. 43. Yöneylem Araştırması ve Endüstri Mühendisliği (YA/EM) Ulusal Kongresi, 1-3 Kasım 2023, Gaziantep, Türkiye.
• Tezcan, B., & Eren, T. (2024). Orman yangınlarında iş sağlığı ve güvenliği uygulamalarının değerlendirilebilmesi için AHP ve ANP yöntemleri ile ölçütlerin belirlenmesi: Türkiye Örneği. Ağaç ve Orman, 5(2), 98–105.
• Tezcan, B., Pınarbaşı, M., Alakaş, H. M., & Eren, T. (2022). Orman Yangını Risk Değerlendirmesine Bulanık Bir Yaklaşım: Ege Bölgesi Örneği. 41. Yöneylem Araştırması ve Endüstri Mühendisliği (YA/EM) Ulusal Kongresi, 26-28 Ekim 2022, Denizli, Türkiye.
• URL-1. (2025). Ministry of Agriculture and Forestry, General Directorate of Forestry. https://www.ogm.gov.tr/tr/e-kutuphane/resmi-istatistikler
• URL-2. (2025). Ministry of Agriculture and Forestry, General Directorate of Forestry. https://www.ogm.gov.tr/tr/orman-yanginlari-oncesi-hazirlik-calismalari
• Van Der Merwe, M., Minas, J. P., Ozlen, M., & Hearne, J. W. (2015). A mixed integer programming approach for asset protection during escaped wildfires. Canadian Journal of Forest Research, 45(4), 444–451.
• Wu, P., Cheng, J., & Feng, C. (2019). Resource‐Constrained Emergency Scheduling for Forest Fires with Priority Areas: An Efficient Integer‐Programming Approach. IEEJ Transactions on Electrical and Electronic Engineering, 14(2), 261–270.
• Yüceşahin, M. M. (1997). Edremit ilçesi (Balıkesir) coğrafyası.
• Zeferino, J. A. (2020). Optimizing the location of aerial resources to combat wildfires: a case study of Portugal. Natural Hazards, 100(3), 1195–1213.
• Zheng, R. (2018). An Optimal Allocation Scheme for City Fire Resources Based on Integer Programming. Proceedings of the Fifth International Forum on Decision Sciences, 101–109.