Q-ROF TOPSIS VE Q-ROF COCOSO YÖNTEMLERİYLE PETROL İSTASYONU YER SEÇİMİ

Yıl 2023, , 1294 - 1309, 30.12.2023

Birsen İrem Kuvvetli

https://doi.org/10.21923/jesd.1245703

Öz

Petrol ve petrol ürünleri dünya ekonomisindeki önemini korumaktadır. Bu sebepten dolayı, petrol ihtiyacını karşılamak adına petrol istasyonu kurulmasına ya da varolan istasyonların iyileştirilmesine ihtiyaç duyulmaktadır. Bu çalışmada, bir petrol istasyonu için tesis yeri seçimi problemi incelenmiştir. Tesis yeri seçimi problemleri Çok Kriterli Karar Verme (ÇKKV) yöntemleri ile çözülmektedir. Literatürdeki çalışmalar incelendiğinde, karar vericilerin yanıtlarındaki belirsizliği çalışmaya doğru şekilde aktarabilmek adına bulanık küme temelli yaklaşımlar tercih edildiği görülmüştür. Bu nedenle bu çalşmada q-seviyeli bulanık küme temelli yöntemler kullanılmıştır. Çalışma gerçek verilerle yapılmış olup, Adana ilinde bir petrol istasyonu kurulumu için yer seçimi problemine çözüm aramaktadır. Çalışma için hem literatürde bulunan hem de özgün olan toplam 10 tane kriter belirlenmiştir. 3 karar verici 5 aday lokasyon arasından seçim yapacaktır. Bu çalışmada q-Rung Orthopair Fuzzy Technique for Order Preference by Similarity to an Ideal Solution (q-ROF TOPSIS) ve q-Rung Orthopair Fuzzy Combined Compromise Solution (q-ROF CoCoSo) yöntemleri kullanılmıştır. Çalışma sonuçları yorumlanmış, duyarlılık analizi yapılarak parametrelerin sonuca etkisi incelenmiş ve çalışma tamamlanmıştır.

Anahtar Kelimeler

Çok Kriterli Karar Verme, TOPSIS, CoCoSo, Q-Seviyeli Ortoper Bulanık Kümeler, Yer Seçimi.

GAS STATION LOCATION SELECTION USING Q-ROF TOPSIS AND Q-ROF COCOSO METHODS

Öz

Petroleum and petroleum products maintain their importance in the world economy. Therefore, there is a need to establish new petrol stations or to improve the existing ones in order to meet the need for oil. In this study, the location selection problem for a petrol station is investigated. Location selection problems are solved by Multi-Criteria Decision Making (MCDM) methods. When the studies in the literature are examined, it is seen that fuzzy set-based approaches are preferred in order to clarify the uncertainty in the answers of the decision makers. Therefore, in this study, q-level fuzzy set-based methods are used to present a more objective ranking approach. In the proposed approach, q-Rung Orthopair Fuzzy Technique for Order Preference by Similarity to an Ideal Solution (q-ROF TOPSIS) and q-Rung Orthopair Fuzzy Combined Compromise Solution (q-ROF CoCoSo) methods were used. The proposed approach is evaluated to solve the site selection problem for a petrol station installation in Adana. Ten criteria, both found in the literature and original, were determined for the study. After weighting the criteria, 3 expert opinions were consulted to rank 5 candidate locations with different station types. The results of the study were interpreted, sensitivity analysis was conducted and the effect of the parameters on the result was examined.

Anahtar Kelimeler

Multi- Criteria Decision Making, TOPSIS, CoCoSo, Q-Rung Orthopair Fuzzy Sets, Location Selection.

Kaynakça

• Abdullah, H. M., Gastli, A., Ben-Brahim, L., & Mohammed, S. O. , 2022. Integrated Multi-Criteria Model for Long-Term Placement of Electric Vehicle Chargers. IEEE Access, 10, 123452-123473.
• Alavipoor, F. S., Karimi, S., Balist, J., & Khakian, A. H., 2016. A geographic information system for gas power plant location using analytical hierarchy process and fuzzy logic. Global Journal of Environmental Science and Management, 2(2), 197-207.
• Atanassov, K.T., 1986. Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 20, 87-96.
• Ayyildiz, E., & Taskin Gumus, A., 2020. A novel spherical fuzzy AHP-integrated spherical WASPAS methodology for petrol station location selection problem: a real case study for İstanbul. Environmental Science and Pollution Research, 27(29), 36109-36120.
• Bustince, H., Barrenechea, E., Fernández, J., Pagola, M., & Montero, J. , 2015. The origin of fuzzy extensions. Springer handbook of computational intelligence, 89-112.
• Deveci, M., Simic, V., & Torkayesh, A. E., 2021. Remanufacturing facility location for automotive lithium-ion batteries: An integrated neutrosophic decision-making model. Journal of Cleaner Production, 317.
• Deveci, M., Pamucar, D., Cali, U., Kantar, E., Kölle, K., & Tande, J. O., 2022. Hybrid q-Rung Orthopair Fuzzy Sets Based CoCoSo Model for Floating Offshore Wind Farm Site Selection in Norway. CSEE Journal of Power and Energy Systems, 8(5), 1261-1280.
• Erbaş, M., Kabak, M., Özceylan, E., & Çetinkaya, C., 2018. Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis. Energy, 163, 1017-1031.
• European Green Deal, 2019. https://ec.europa.eu/info/sites/info/files/european-green-deal-communication_en.pdf. Erişim tarihi: 29.09.2022.
• Fakhari, F., Tavakkoli-Moghaddam, R., Tohidifard, M., & Ghaderi, S. F., 2019. Location Optimization of Gas Power Plants by a Z-Number Data Envelopment Analysis. In Optimization of Complex Systems: Theory, Models, Algorithms and Applications (pp. 926-936). Springer International Publishing.
• Feng, J., Xu, S. X., & Li, M., 2021. A novel multi-criteria decision-making method for selecting the site of an electric-vehicle charging station from a sustainable perspective. Sustainable Cities and Society, 65.
• Kannan, D., Moazzeni, S., Darmian, S. M. & Afrasiabi, A., 2020. A hybrid approach based on MCDM methods and Monte Carlo simulation for sustainable evaluation of potential solar sites in east of Iran. Journal of Cleaner Production, 279.
• Karagoz, S., Deveci, M., Simic, V., Aydin, N. & Bolukbas, U., 2020. A novel intuitionistic fuzzy MCDM-based CODAS approach for locating an authorized dismantling center: A case study of Istanbul. Waste Management & Research, 38(6), 1-13.
• Karagöz, S., Deveci, M., Simic, V. &Aydin, N., 2021. Interval type-2 fuzzy ARAS method for recycling facility location problems. Applied Soft Computing, 102.
• Karande, P. ve Chatterjee, P., 2018. Desirability function approach for selection of facility location: A case study. IEOM Society International, 1700-1708.
• Kumar, R., Athawale, V. M. ve Chakraborty, S., 2010. Facility location selection using the UTA method. The IUP Journal of Operations Management, 9(4), 21-34.
• Liu, H. C., Yang, M., Zhou, M., & Tian, G., 2018. An integrated multi-criteria decision making approach to location planning of electric vehicle charging stations. IEEE Transactions on Intelligent Transportation Systems, 20(1), 362-373.
• Liu, P., & Wang, P. , 2018. Some q‐rung orthopair fuzzy aggregation operators and their applications to multiple‐attribute decision making. International Journal of Intelligent Systems, 33(2), 259-280.
• Li, S., Su, B., St-Pierre, D. L., Sui, P. C., Zhang, G., & Xiao, J., 2017. Decision-making of compressed natural gas station siting for public transportation: Integration of multi-objective optimization, fuzzy evaluating, and radar charting. Energy, 140, 11-17.
• MirHassani, S. A., & Ebrazi, R., 2013. A flexible reformulation of the refueling station location problem. Transportation Science, 47(4), 617-628.
• Mokhtarian, M., 2011. A new fuzzy weighted average (FWA) method based on left and right scores: An application for determining a suitable location for a gas oil station. Computers & Mathematics with Applications, 61(10), 3136-3145.
• Njoku, C. G., & Alagbe, A. O., 2015. Site suitability assessment of petrol filling stations (PFSs) in Oyo Town, Oyo State, Nigeria: a geographic information systems (GIS) approach. ISOR Journal of Environmental Science, Technology and food Technology (IOSR-JESTFT) e-ISSN, 2319-2402.
• Otay, I., Atik, S., 2021. Multi-criteria Oil Station Location Evaluation Using Spherical AHP&WASPAS: A Real-Life Case Study. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I., Cebi, S., Tolga, A. (edt.) Intelligent and Fuzzy Techniques: Smart and Innovative Solutions. INFUS 2020. Advances in Intelligent Systems and Computing, vol 1197, içinde (s. 591-598) Springer, Cham.
• Peng, X., & Huang, H., 2020. Fuzzy decision making method based on CoCoSo with critic for financial risk evaluation. Technological and Economic Development of Economy, 26(4), 695.
• Pinar, A., Boran, F. E., 2020. A q-rung orthopair fuzzy multi-criteria group decision making method for supplier selection based on a novel distance measure. International Journal of Machine Learning and Cybernetics, 11, 1749-1780.
• Pınar, A. 2021. Üçüncü Parti Lojistik Firma Seçiminde Q Seviyeli Bulanık TOPSIS Uygulaması. Journal of the Turkish Operations Management (JTOM), 5(1), 588-597.
• Semih, T., Seyhan, S., 2011. A multi-criteria factor evaluation model for gas station site selection. evaluation, 2(1), 12-21.
• Şeker, S. ve Aydin, N., 2020. Hydrogen production facility location selection for Black Sea using entropy based TOPSIS under IVPF environment. International Journal of Hydrogen Energy, 45(32), 15855-15868.
• Toksoy Erdoğan, M., 2012. Çok Nitelikli Karar Verme Yöntemleri ve VIKOR Yöntemi ile bir Uygulama, Yüksek Lisans Tezi, İstanbul Üniversitesi, İstanbul.
• Torkayesh, A. E. ve Simic, V., 2022. Stratified hybrid decision model with constrained attributes: Recycling facility location for urban healthcare plastic waste. Sustainable Cities and Society, 77.
• Tripathi, A. K., Agrawal, S. & Gupta, R. D., 202. Comparison of GIS-based AHP and fuzzy AHP methods for hospital site selection: A case study for Prayagraj City, India. GeoJournal, 87.
• Wang, R., Li, Y., 2018. A novel approach for green supplier selection under a q-rung orthopair fuzzy environment. Symmetry, 10(12), 687.
• Xuan, H. A., Trinh, V. V., Kuaanan, T. & Phoungthong, K., 2022. Use of hybrid MCDM methods for site location of solar-powered hydrogen production plants in Uzbekistan. Sustainable Energy Technologies and Assessments, 52.
• Yeşilkaya, M., 2018. Çok ölçütlü karar verme yöntemleri ile kağıt fabrikası kuruluş yeri seçimi. Çukurova Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 33(4), 31-44.
• Yıldızhan, D., Erenoğlu, AK., Erdinç, O., 2022. Elektrikli Araç Entegrasyonunun Dağıtım Sistemine Etkilerinin İncelenmesi ve Şarj İstasyonu Altyapısının Tayin Edilmesi, Mühendislik Bilimleri ve Tasarım Dergisi, 10(4), 1232- 1242.
• Yager, R. R., Alajlan, N., 2017. Approximate Reasoning With Generalized Orthopair Fuzzy Sets. Information Fusion, 38, 65-73.
• Zadeh, L. A., 1965. Fuzzy sets. Information and control, 8(3), 338-353.
• Zhao, H., Li, N., 2016. Optimal siting of charging stations for electric vehicles based on fuzzy Delphi and hybrid multi-criteria decision-making approaches from an extended sustainability perspective. Energies, 9(4), 270.
• Zhu, H., Zhao, J., & Li, H. , 2022. Q-ROF-SIR methods and their applications to multiple attribute decision making. International Journal of Machine Learning and Cybernetics, 1-13.