An Integrated PIPRECIA and COPRAS Method under Fuzzy Environment: A Case of Truck Tractor Selection

Özdağoğlu, Aşkın; Öztaş, Gülin Zeynep; Keleş, Murat Kemal; Genç, Volkan

doi:http://dx.doi.org/10.17093/alphanumeric.1005970

An Integrated PIPRECIA and COPRAS Method under Fuzzy Environment: A Case of Truck Tractor Selection

Aşkın Özdağoğlu, Ph.D.

Gülin Zeynep Öztaş, Ph.D.

Murat Kemal Keleş, Ph.D.

Volkan Genç

Abstract

Selecting the right truck tractor is critical for logistics companies involved in road freight transportation. Determining the criteria that are effective in the selection of truck tractors and then evaluating the alternatives are the main objectives of this study. In this context, a hybrid Multi-Criteria Decision-Making model composed of Fuzzy PIPRECIA (F-PIPRECIA) and Fuzzy COPRAS (F-COPRAS) methods is proposed to be used in the selection of truck tractors. In the related literature, no studies that applied F-PIPRECIA and F-COPRAS together to determine the best truck tractor have been published yet. In this regard, this study is thought to contribute to the literature in terms of the methods used and the application of truck tractor selection. Moreover, the findings of this study will pave the way for those who conduct academic studies and the authorities of companies involved in road transport in the logistics sector.

Keywords: F-COPRAS, F-PIPRECIA, Multi Criteria Decision Making, Truck Tractor Selection

Jel Classification: C46

Suggested citation

Özdağoğlu, A., Öztaş, G. Z., Keleş, M. K. & Genç, V. (2021). An Integrated PIPRECIA and COPRAS Method under Fuzzy Environment: A Case of Truck Tractor Selection. Alphanumeric Journal, 9(2), 269-298. http://dx.doi.org/10.17093/alphanumeric.1005970

bibtex

References

Alkan, Ö., & Albayrak, Ö. K. (2020). Ranking of renewable energy sources for regions in Turkey by fuzzy entropy-based fuzzy COPRAS and fuzzy MULTIMOORA. Renewable Energy, 162, 712-726. https://doi.org/10.1016/j.renene.2020.08.062
Amoozad Mahdiraji, H., Arzaghi, S., Stauskis, G., & Zavadskas, E. K. (2018). A hybrid fuzzy BWM-COPRAS method for analyzing key factors of sustainable architecture. Sustainability, 10(5), 1626.
Ansari, Z. N., Kant, R., & Shankar, R. (2020). Evaluation and ranking of solutions to mitigate sustainable remanufacturing supply chain risks: a hybrid fuzzy SWARA-fuzzy COPRAS framework approach. International Journal of Sustainable Engineering, 13(6), 473-494. https://doi.org/10.1080/19397038.2020.1758973
Apak, S., Göğüş, G. G., & Karakadılar, İ. S. (2012). An Analytic Hierarchy Process Approach with a Novel Framework for Luxury Car Selection. Procedia - Social and Behavioral Sciences, 58, 1301–1308. https://doi.org/10.1016/j.sbspro.2012.09.1113
Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87-96.
Aytekin, A., and Durucasu,H. (2021). Nearest solution to references method for multicriteria decision-making problems. Decision Science Letters 10(2), 111-128.
Başaran, B., & Çakir, S. (2021). Evaluation of food safety and halal criteria in supplier selection: an application in the food sector with fuzzy COPRAS method. International Food Research Journal, 28(3), 576-585. ISSN: 19854668
Baykaşoğlu, A. (2010). Çok kriterli TIR çekici seçimi. LODER Lojistik Dergisi, 14, 32-36.
Baykasoğlu, A., & Golcuk, I. (2014). Çok kriterli tır çekici seçiminde bulanık integral yaklaşımı. 3. Ulusal Lojistik ve Tedarik Zinciri Kongresi, Karadeniz Teknik Üniversitesi, 15-17 Mayıs 2014, Trabzon, 776-786.
Baykaşoğlu, A., Dereli, T., Altun, K., (2011). Kullanılmış tır çekici seşimi: pratik bir karar destek yaklaşımı EVEN-SWAP. Lojistik, 19, 18-22
Baykaşoğlu, A., Kaplanoğlu, V., Durmuşoğlu, Z. D., & Şahin, C. (2013). Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection. Expert Systems with Applications, 40(3), 899-907. https://doi.org/10.1016/j.eswa.2012.05.046
Blagojević, A., Kasalica, S., Stević, Ž., Tričković, G., & Pavelkić, V. (2021). Evaluation of safety degree at railway crossings in order to achieve sustainable traffic management: A novel integrated fuzzy MCDM model. Sustainability, 13(2), 832. https://doi.org/10.3390/su13020832
Blagojević, A., Stević, Ž., Marinković, D., Kasalica, S., & Rajilić, S. (2020). A novel entropy-fuzzy PIPRECIA-DEA model for safety evaluation of railway traffic. Symmetry, 12(9), 1479. https://doi.org/10.3390/sym12091479
Chakraborty, S., & Prasad, K. (2016). A QFD-based expert system for industrial truck selection in manufacturing organizations. Journal of Manufacturing Technology Management, 27(6), 800-817. https://doi.org/10.1108/JMTM-02-2016-0020
Chan, F. T., Jha, A., & Tiwari, M. K. (2016). Bi-objective optimization of three echelon supply chains involving truck selection and loading using NSGA-II with heuristics algorithm. Applied Soft Computing, 38, 978-987.
Chand, M., & Avikal, S. 2016. An MCDM based approach for purchasing a car from Indian car market. 4th Students Conference on Engineering and Systems, SCES 2015, 8–11. https://doi.org/10.1109/SCES.2015.7506454
Đalić, I., Stević, Ž., Karamasa, C., & Puška, A. (2020). A novel integrated fuzzy PIPRECIA–interval rough SAW model: Green supplier selection. Decision Making: Applications in Management and Engineering, 3(1), 126-145. DOI: https://doi.org/10.31181/dmame2003114d
de Sousa Junior, W. T., Souza, M. J. F., Cabral, I. E., & Diniz, M. E. (2014). Multi-Criteria Decision Aid methodology applied to highway truck selection at a mining company. Rem: Revista Escola de Minas, 67(3), 285-290.
Dhiman, H. S., & Deb, D. (2020). Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms. Energy, 202, 117755. https://doi.org/10.1016/j.energy.2020.117755
Doğan, E. M., Eren, M., & Çelik, K. (2017). Lojistik sektöründe ağır ticari araç seçimi problemine yönelik COPRAS-G yöntemi ile karar verme. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 19(1), 153-178.
Garg, R., Kumar, R., & Garg, S. (2019). MADM-based parametric selection and ranking of E-learning websites using fuzzy COPRAS. IEEE Transactions on Education, 62(1), 11-18. https://doi.org/10.1109/TE.2018.2814611
Görçün, Ö. F. (2019). Uluslararası Taşımacılık İşletmelerinin Çekici Araç Seçimlerinin Entegre AHP, Entropi ve TOPSIS Yöntemleri Kullanılarak Analizi. Afyon Kocatepe Üniversitesi Sosyal Bilimler Dergisi, 21(3), 899-922. https://doi.org/10.32709/akusosbil.521611
Hasheminezhad, A., Hadadi, F., & Shirmohammadi, H. (2021). Investigation and prioritization of risk factors in the collision of two passenger trains based on fuzzy COPRAS and fuzzy DEMATEL methods. Soft Computing, 25, 4677-4697. https://doi.org/10.1007/s00500-020-05478-3
Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management, 11(2), 243-258.
Khorasani, S. T. (2018). Green supplier evaluation by using the integrated fuzzy AHP model and fuzzy copras. Process Integration and Optimization for Sustainability, 2(1), 17-25. https://doi.org/10.1007/s41660-017-0027-9
Marković, V., Stajić, L., Stević, Ž., Mitrović, G., Novarlić, B., & Radojičić, Z. (2020). A novel integrated subjective-objective mcdm model for alternative ranking in order to achieve business excellence and sustainability. Symmetry, 12(1), 164. https://doi.org/10.3390/SYM12010164
Mumani, A., & Maghableh, G. (2021). An integrated ANP-ELECTRE III decision model was applied to eco-friendly car selection. Journal of Engineering Research.
Nedeljković, M., Puška, A., Doljanica, S., Virijević Jovanović, S., Brzaković, P., Stević, Ž., & Marinkovic, D. (2021). Evaluation of rapeseed varieties using novel integrated fuzzy PIPRECIA–Fuzzy MABAC model. Plos one, 16(2), e0246857. https://doi.org/10.1371/journal.pone.0246857
Oztaysi, B., Onar, S. C., & Kahraman, C. (2021). Electric Vehicle Selection by Using Fuzzy KEMIRA. Journal of Multiple-Valued Logic & Soft Computing, 37.
Ömürbek, N., Karaatli, M., Eren, H., & Şanli, B. (2014). Ahp Temelli Promethee Siralama Yöntemi İle Hafif Ticari Araç Seçimi. Suleyman Demirel University Journal of Faculty of Economics & Administrative Sciences, 19(4).
Patil, A. N., Bhale, N. G. P., Raikar, N., & Prabhakaran, M. (2017). Car Selection Using Hybrid Fuzzy AHP and Grey Relation Analysis Approach. International Journal of Performability Engineering, 13(5), 569.
Puška, A., Nedeljković, M., Hashemkhani Zolfani, S., & Pamučar, D. (2021). Application of interval fuzzy logic in selecting a sustainable supplier on the example of agricultural production. Symmetry, 13(5), 774. https://doi.org/10.3390/sym13050774
Roozbahani, A., Ghased, H., & Shahedany, M. H. (2020). Inter-basin water transfer planning with grey COPRAS and fuzzy COPRAS techniques: A case study in Iranian Central Plateau. Science of the Total Environment, 726, 138499. https://doi.org/10.1016/j.scitotenv.2020.138499
Roy, S., Mohanty, S., & Mohanty, S. (2018, August). An efficient hybrid MCDM based approach for car selection in automobile industry. In 2018 International Conference on Research in Intelligent and Computing in Engineering (RICE) (pp. 1-5). IEEE.
Sarkar, A., Ghosh, A., Karmakar, B., Shaikh, A., & Mondal, S. P. (2020, November). Application of Fuzzy TOPSIS Algorithm for Selecting Best Family Car. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 59-63). IEEE.
Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill.
Shaikh, A., Singh, A., Ghose, D., & Shabbiruddin. (2020). Analysis and selection of optimum material to improvise braking system in automobiles using integrated Fuzzy-COPRAS methodology. International Journal of Management Science and Engineering Management, 15(4), 265-273. https://doi.org/10.1080/17509653.2020.1772895
Singh, R., & Avikal, S. (2019). An MCDM-Based Approach for Selection of a Sedan Car from Indian Car Market. In Harmony Search and Nature Inspired Optimization Algorithms (pp. 569-578). Springer, Singapore.
Smarandache, F. (1999). A unifying field in Logics: Neutrosophic Logic. In Philosophy (pp. 1-141). American Research Press.
Smarandache, F. (2017). Plithogeny, Plithogenic Set, Logic, Probability, and Statistics. Pons, Belgium
Stanković, M., Stević, Ž., Das, D. K., Subotić, M., & Pamučar, D. (2020). A new fuzzy MARCOS method for road traffic risk analysis. Mathematics, 8(3), 457. https://doi.org/10.3390/MATH8030457
Stanujkić, D., Zavadskas, E. K., Karabasevic, D., Smarandache, F., & Turskis, Z. (2017). The Use Of The Pıvot Paırwıse Relatıve Crıterıa Importance Assessment Method For Determınıng The Weıghts Of Crıterıa. Journal for Economic Forecasting, (4), 116-133.
Stanujkić, D., Karabašević, D., Popović, G., Stanimirović, P. S., Saračević, M., Smarandache, F., ... & Ulutaş, A. (2021). A New Grey Approach for Using SWARA and PIPRECIA Methods in a Group Decision-Making Environment. Mathematics, 9(13), 1554.
Stevic, Ž., Stjepanovic, Ž., Božickovic, Z., Das, D. K., & Stanujkic, D. (2018). Assessment of Conditions for Implementing Information Technology in a Warehouse System: A Novel Fuzzy PIPRECIA Method. Symmetry, 10 (586), 1-28. https://doi.org/10.3390/sym10110586.
Tolga, A. C., & Durak, G. (2019). Evaluating innovation projects in air cargo sector with fuzzy COPRAS. In International Conference on Intelligent and Fuzzy Systems (pp. 702-710). Springer, Cham. https://doi.org/10.1007/978-3-030-23756-1_84
Tomašević, M., Lapuh, L., Stević, Ž., Stanujkić, D., & Karabašević, D. (2020). Evaluation of criteria for the implementation of high-performance computing (HPC) in Danube Region countries using fuzzy PIPRECIA method. Sustainability, 12(7), 3017. https://doi.org/10.3390/su12073017
Ulutaş, A., Popovic, G., Radanov, P., Stanujkic, D., & Karabasevic, D. (2021). A new hybrid fuzzy PSI-PIPRECIA-CoCoSo MCDM based approach to solving the transportation company selection problem. Technological and Economic Development of Economy, 27(5), 1227-1249.
Vesković, S., Milinković, S., Abramović, B., & Ljubaj, I. (2020). Determining criteria significance in selecting reach stackers by applying the fuzzy PIPRECIA method. Operational Research in Engineering Sciences: Theory and Applications, 3(1), 72-88. https://doi.org/10.31181/oresta2001072v
Vesković, S., Stević, Ž., Karabašević, D., Rajilić, S., Milinković, S., & Stojić, G. (2020). A new integrated fuzzy approach to selecting the best solution for a business balance of passenger rail operator: Fuzzy PIPRECIA-fuzzy EDAS model. Symmetry, 12(5), 743.
Yazdani, Morteza., Alidoosti, Ali., Zavadskas, Edmundas, K. (2011). Risk Analysis of Critical Infrastructures Using Fuzzy Copras, Economic Research-Ekonomska Istraživanja, 24(4), 27-40, https://doi.org/10.1080/1331677X.2011.11517478.
Zadeh, L. A. (1965). Fuzzy Sets. Information and Contro. 8. 338-353
Zarbakhshnia, N., Soleimani, H., & Ghaderi, H. (2018). Sustainable third-party reverse logistics provider evaluation and selection using fuzzy SWARA and developed fuzzy COPRAS in the presence of risk criteria. Applied Soft Computing, 65, 307-319. https://doi.org/10.1016/j.asoc.2018.01.023
Zavadskas, E. K., A. Kaklauskas, and V. Sarka. 1994. “The New Method of Multicriteria Complex Proportional Assessment of Projects.” Technological and Economic Development of Economy 1 (3): 131–139.