Facility Location Selection for Ammunition Depots based on GIS and Pythagorean Fuzzy WASPAS
DOI:
https://doi.org/10.31181/jopi2120247Keywords:
Pythagorean Fuzzy Sets, WASPAS, GIS, Ammunition, Recycling, Facility Location SelectionAbstract
The purpose of this study is to determine depot locations where expired ammunition will be controlled before being sent to recycling facilities. Expiration of ammunition means that using, transporting and even storing that ammunition where it is located poses a greater risk. For this reason, it is important to determine facility locations so that ammunition is stored in places that will least harm the environment and human health. The criteria to be used for ammunition depot location selection were determined through literature review, various researches and expert opinions. The proposed model is based on the combined use of Geographic information system (GIS) and multi-criteria decision making. For an example application of the model, a generic study on a district basis in Turkey is presented. Candidate depot locations were determined using GIS with the help of 6 main criteria and 18 sub-criteria. Then, candidate depot locations were ranked by the Pythagorean Fuzzy Set-based WASPAS (Weighted Aggregated Sum Product Assessing) method, taking into account the opinions of military experts for the main criteria. WASPAS method selected location A1 as the most suitable ammunition depot location. The results show that the proposed methodology can be practically applied.
Downloads
References
Owen, S. H., & Daskin, M. S. (1998). Strategic facility location: A review. European journal of operational research, 111(3), 423-447. https://doi.org/10.1016/S0377-2217(98)00186-6
Kuby, M. J. (1987). Programming models for facility dispersion: The p‐dispersion and maxisum dispersion problems. Geographical Analysis, 19(4), 315-329. https://doi.org/10.1111/j.1538-4632.1987.tb00133.x
Erkut, E., & Neuman, S. (1991). Comparison of four models for dispersing facilities. INFOR: Information Systems and Operational Research, 29(2), 68-86. https://doi.org/10.1080/03155986.1991.11732157
Nato Internatıonal Staff - Defence Investment Dıvısıon. Manual of nato safety prıncıples for the stroge of mılıtary ammunıtıon and explosıves. http://www.rasrinitiative.org/pdfs/AASTP-1-Ed1-Chge-3-Public-Release-110810.pdf.
Akar O. (2018). Mühimmat Kaza Nedenlerinin İncelenmesi ve Mühimmatın Depolanmasında Risk Değerlendirmesi. Yayınlanmamış Doctoral dissertation, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Kazaların Çevresel ve Teknik Araştırması Anabilim Dalı, Ankara.
Daskin, M. (1997). Network and discrete location: models, algorithms and applications. Journal of the Operational Research Society, 48(7), 763-764. https://doi.org/10.1057/palgrave.jors.2600828
Klose, A., & Drexl, A. (2005). Facility location models for distribution system design. European journal of operational research, 162(1), 4-29. https://doi.org/10.1016/j.ejor.2003.10.031
Arabani, A. B., & Farahani, R. Z. (2012). Facility location dynamics: An overview of classifications and applications. Computers & Industrial Engineering, 62(1), 408-420. https://doi.org/10.1016/j.cie.2011.09.018
Revelle, C. S., Eiselt, H. A., & Daskin, M. S. (2008). A bibliography for some fundamental problem categories in discrete location science. European journal of operational research, 184(3), 817-848.
https://doi.org/10.1016/j.ejor.2006.12.044
Bastı, M. (2012). P-medyan tesis yeri seçim problemi ve çözüm yaklaşımları. AJIT-e: Academic Journal of Information Technology, 3(7), 47-75. https://doi.org/10.5824/1309-1581.2012.2.004.x
Ağdaş M. (2014). Çok Kriterli Karar Verme Yöntemleri ile Lojistik Tesis Yer Seçimi: Kamu Sektöründe Bir Uygulama. Doctoral dissertation, Kara Harp Okulu Savunma Bilimleri Enstitüsü Tedarik ve Lojistik Yönetimi Ana Bilim Dalı, Ankara.
Chou, T. Y., Hsu, C. L., & Chen, M. C. (2008). A fuzzy multi-criteria decision model for international tourist hotels location selection. International journal of hospitality management, 27(2), 293-301. https://doi.org/10.1016/j.ijhm.2007.07.029
Farahani, R. Z., & Hekmatfar, M. (Eds.). (2009). Facility location: concepts, models, algorithms and case studies. Springer Science & Business Media. https://doi.org/10.1007/978-3-7908-2151-2
Shahparvari, S., Nasirian, A., Mohammadi, A., Noori, S., & Chhetri, P. (2020). A GIS-LP integrated approach for the logistics hub location problem. Computers & Industrial Engineering, 146, 106488. https://doi.org/10.1016/j.cie.2020.106488
Alizadeh, B., & Afrashteh, E. (2020). Budget-constrained inverse median facility location problem on tree networks. Applied Mathematics and Computation, 375, 125078. https://doi.org/10.1016/j.amc.2020.125078
Ashrafzadeh, M., Rafiei, F. M., Isfahani, N. M., & Zare, Z. (2012). Application of fuzzy TOPSIS method for the selection of Warehouse Location: A Case Study. Interdisciplinary journal of contemporary research in business, 3(9), 655-671.
Chukwuma, E. C. (2019). Facility location allocation modelling for bio-energy system in Anambra State of Nigeria: Integration of GIS and location model. Renewable Energy, 141, 460-467.
https://doi.org/10.1016/j.renene.2019.04.022
Abareshi, M., & Zaferanieh, M. (2019). A bi-level capacitated P-median facility location problem with the most likely allocation solution. Transportation Research Part B: Methodological, 123, 1-20. https://doi.org/10.1016/j.trb.2019.03.013
Colson, G., & Dorigo, F. (2004). A public warehouses selection support system. European Journal of Operational Research, 153(2), 332-349.https://doi.org/10.1016/S0377-2217(03)00156-5
Gigović, L., Pamučar, D., Bajić, Z., & Milićević, M. (2016). The combination of expert judgment and GIS-MAIRCA analysis for the selection of sites for ammunition depots. Sustainability, 8(4), 372. https://doi.org/10.3390/su8040372
Kabak, M., & Keskin, İ. (2018). Hazardous materials depots selection based on GIS and MCDM. Arabian Journal for Science and Engineering, 43, 3269-3278. https://doi.org/10.1007/s13369-018-3063-z
Feyzi, S., Khanmohammadi, M., Abedinzadeh, N., & Aalipour, M. (2019). Multi-criteria decision analysis FANP based on GIS for siting municipal solid waste incineration power plant in the north of Iran. Sustainable Cities and Society, 47, 101513. https://doi.org/10.1016/j.scs.2019.101513
Akgün, İ., & Erdal, H. (2019). Solving an ammunition distribution network design problem using multi-objective mathematical modeling, combined AHP-TOPSIS, and GIS. Computers & Industrial Engineering, 129, 512-528.
https://doi.org/10.1016/j.cie.2019.02.004
Nyimbili, P. H., & Erden, T. (2020). GIS-based fuzzy multi-criteria approach for optimal site selection of fire stations in Istanbul, Turkey. Socio-Economic Planning Sciences, 71, 100860. https://doi.org/10.1016/j.seps.2020.100860
Chabok, M., Asakereh, A., Bahrami, H., & Jaafarzadeh, N. O. (2020). Selection of MSW landfill site by fuzzy-AHP approach combined with GIS: Case study in Ahvaz, Iran. Environmental Monitoring and Assessment, 192, 1-15.
https://doi.org/10.1007/s10661-020-08395-y
Nyimbili, P. H., & Erden, T. (2021). Comparative evaluation of GIS-based best-worst method (BWM) for emergency facility planning: perspectives from two decision-maker groups. Natural Hazards, 105, 1031-1067.
https://doi.org/10.1007/s11069-020-04348-3
Danesh, G., Monavari, M., Omrani, G., Karbasi, A., & Farsad, F. (2021). Application of Multi-Criteria Decision-Making Models Based on Geographic Information Systems in Locating Hazardous Waste Disposal Sites (Case Study: Bushehr Province). Journal of Environmental Science and Technology, 23(3), 87-101. 10.30495/JEST.2021.40769.4500
Ali, S. A., Parvin, F., Al-Ansari, N., Pham, Q. B., Ahmad, A., Raj, M. S., ... & Thai, V. N. (2021). Sanitary landfill site selection by integrating AHP and FTOPSIS with GIS: a case study of Memari Municipality, India. Environmental Science and Pollution Research, 28, 7528-7550. https://doi.org/10.1007/s11356-020-11004-7
Gil-García, I. C., Ramos-Escudero, A., García-Cascales, M. S., Dagher, H., & Molina-García, A. (2022). Fuzzy GIS-based MCDM solution for the optimal offshore wind site selection: The Gulf of Maine case. Renewable Energy, 183, 130-147. https://doi.org/10.1016/j.renene.2021.10.058
Ghoushchi, S. J., & Nasiri, B. (2022). Sustainable landfill site selection for hazardous waste using a GIS-based MCDM approach with G-number information. Environment, Development and Sustainability, 1-32.
https://doi.org/10.1007/s10668-022-02400-9
Durlević, U., Novković, I., Carević, I., Valjarević, D., Marjanović, A., Batoćanin, N., ... & Valjarević, A. (2023). Sanitary landfill site selection using GIS-based on a fuzzy multi-criteria evaluation technique: a case study of the City of Kraljevo, Serbia. Environmental Science and Pollution Research, 30(13), 37961-37980.
https://doi.org/10.1007/s11356-022-24884-8
Shao, M., Zhao, Y., Sun, J., Han, Z., & Shao, Z. (2023). A decision framework for tidal current power plant site selection based on GIS-MCDM: A case study in China. Energy, 262, 125476.
https://doi.org/10.1016/j.energy.2022.125476
Mozaffari, M., Bemani, A., Erfani, M., Yarami, N., & Siyahati, G. (2023). Integration of LCSA and GIS-based MCDM for sustainable landfill site selection: a case study. Environmental monitoring and assessment, 195(4), 510.
https://doi.org/10.1007/s10661-023-11112-0
Görçün, Ö. F., Pamucar, D., Krishankumar, R., & Küçükönder, H. (2023). The selection of appropriate Ro-Ro Vessel in the second-hand market using the WASPAS'Bonferroni approach in type 2 neutrosophic fuzzy environment. Engineering Applications of Artificial Intelligence, 117, 105531. https://doi.org/10.1016/j.engappai.2022.105531
Pamucar, D., Torkayesh, A. E., Deveci, M., & Simic, V. (2022). Recovery center selection for end-of-life automotive lithium-ion batteries using an integrated fuzzy WASPAS approach. Expert Systems with Applications, 206, 117827. https://doi.org/10.1016/j.eswa.2022.117827
Aytekin, A., Görçün, Ö. F., Ecer, F., Pamucar, D., & Karamaşa, Ç. (2022). Evaluation of the pharmaceutical distribution and warehousing companies through an integrated Fermatean fuzzy entropy-WASPAS approach. Kybernetes. https://doi.org/10.1108/K-04-2022-0508
Deveci, M., Pamucar, D., Gokasar, I., Isik, M., & Coffman, D. M. (2022). Fuzzy Einstein WASPAS approach for the economic and societal dynamics of the climate change mitigation strategies in urban mobility planning. Structural Change and Economic Dynamics, 61, 1-17. https://doi.org/10.1016/j.strueco.2022.01.009
Deveci, M., Gokasar, I., Pamucar, D., Coffman, D. M., & Papadonikolaki, E. (2022). Safe E-scooter operation alternative prioritization using a q-rung orthopair Fuzzy Einstein based WASPAS approach. Journal of Cleaner Production, 347, 131239. https://doi.org/10.1016/j.jclepro.2022.131239.
Deveci, M., Öner, S. C., Ciftci, M. E., Özcan, E., & Pamucar, D. (2022). Interval type-2 hesitant fuzzy Entropy-based WASPAS approach for aircraft type selection. Applied Soft Computing, 114, 108076.
https://doi.org/10.1016/j.asoc.2021.108076
Yager, R. R., & Abbasov, A. M. (2013). Pythagorean membership grades, complex numbers, and decision making. International Journal of Intelligent Systems, 28(5), 436-452. https://doi.org/10.1002/int.21584
Deveci, M., Eriskin, L., & Karatas, M. (2021). A survey on recent applications of pythagorean fuzzy sets: A state-of-the-art between 2013 and 2020. Pythagorean Fuzzy Sets: Theory and Applications, 3-38. https://doi.org/10.1007/978-981-16-1989-2_1
Zhang, X., & Xu, Z. (2014). Extension of TOPSIS to multiple criteria decision making with Pythagorean fuzzy sets. International journal of intelligent systems, 29(12), 1061-1078. https://doi.org/10.1002/int.21676
Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir elektrotechnika, 122(6), 3-6.https://doi.org/10.5755/j01.eee.122.6.1810
Madić, M., Gecevska, V., Radovanović, M., & Petković, D. (2014). Multi-criteria economic analysis of machining processes using the WASPAS method. Journal of Production Engineering, 17(2), 1-6.
Lashgari, S., Antuchevičienė, J., Delavari, A., & Kheirkhah, O. (2014). Using QSPM and WASPAS methods for determining outsourcing strategies. Journal of Business Economics and Management, 15(4), 729-743. https://doi.org/10.3846/16111699.2014.908789
Çetinkaya, C., Erbaş, M., Kabak, M., & Özceylan, E. (2023). A mass vaccination site selection problem: An application of GIS and entropy-based MAUT approach. Socio-Economic Planning Sciences, 85, 101376. https://doi.org/10.1016/j.seps.2022.101376
Gil-García, I. C., Ramos-Escudero, A., García-Cascales, M. S., Dagher, H., & Molina-García, A. (2022). Fuzzy GIS-based MCDM solution for the optimal offshore wind site selection: The Gulf of Maine case. Renewable Energy, 183, 130-147. https://doi.org/10.1016/j.renene.2021.10.058
Shao, M., Zhao, Y., Sun, J., Han, Z., & Shao, Z. (2023). A decision framework for tidal current power plant site selection based on GIS-MCDM: A case study in China. Energy, 262, 125476. https://doi.org/10.1016/j.energy.2022.125476
Eldamaty, T., Ahmed, A. G., & Helal, M. M. (2023). GIS-Based Multi Criteria Analysis for Solar Power Plant Site Selection Support in Mecca. Engineering, Technology & Applied Science Research, 13(3), 10963-10968. https://doi.org/10.48084/etasr.5927
Şahin, E. K. (2010). Perakende Marketlerin Yer Seçimine Yönelik Cbs Uygulamasi. III.Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu, Gebze, Kocaeli.
Akyol, A. (2003). Nükleer, biyolojik ve kimyasal korunma donanımı depolarının yerlerinin matematiksel modelleme ile tespiti. Doctoral dissertation, Kara Harp Okulu Savunma Bilimleri Enstitüsü Harekat Araştırması Ana Bilim Dalı, Ankara.
Altuntaş, A. (2017). Dağıtık yerleşkeli patlayıcı madde depo yer seçimi ve uygulaması. Doctoral dissertation, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Tedarik ve Lojistik Yönetimi Anabilim Dalı, Ankara.
Ramshani, M., Ostrowski, J., Zhang, K., & Li, X. (2019). Two level uncapacitated facility location problem with disruptions. Computers & industrial engineering, 137, 106089. https://doi.org/10.1016/j.cie.2019.106089
North, J., & Miller, F. L. (2017). Facility location using GIS enriched demographic and lifestyle data for a traveling entertainment troupe in Bavaria, Germany. Decision Support Systems, 99, 30-36. https://doi.org/10.1016/j.dss.2017.05.007
Vasileiou, M., Loukogeorgaki, E., & Vagiona, D. G. (2017). GIS-based multi-criteria decision analysis for site selection of hybrid offshore wind and wave energy systems in Greece. Renewable and sustainable energy reviews, 73, 745-757. https://doi.org/10.1016/j.rser.2017.01.161
AASTP, N. (2010). Manual of NATO safety principles for the storage of military ammunition and explosives. Ammunition Safety Group, NATO, 588.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Journal of Operations Intelligence
This work is licensed under a Creative Commons Attribution 4.0 International License.