A new hybrid fuzzy PSI-PIPRECIA-CoCoSo MCDM based approach to solving the transportation company selection problem
Abstract
Nowadays, customers are not only interested in the quality of products, but they also want to have these products in a timely manner. The managers of an organization are faced with two problems when the distribution of products is in question, namely: (1) customers are usually geographically dispersed and (2) transportation should be performed in a cost-effective way. Although managers may have a significant experience and formal knowledge, decisions connected with the selection of an appropriate transportation company may very often be biased. For the purpose of avoiding making the inadequate decisions that might harm the operation of the organization, the application of a hybrid MCDM model is proposed in this paper. The proposed model consists of three fuzzy MCDM methods, including: the PIPRECIA, the PSI, and the CoCoSo methods. The fuzzy-PIPRECIA method is used to achieve the subjective weights of criteria, whereas the fuzzy-PSI method is used to obtain the objective weights of criteria. Fuzzy-CoCoSo is utilized to rank alternative transportation companies according to their performances. The possibilities of the proposed hybrid model are tested on a real case study pointed at the selection of an appropriate company for the transportation of ready-garments to retailers in Turkey.
First published online 07 July 2021
Keyword : MCDM, fuzzy PIPRECIA method, fuzzy PSI method, fuzzy CoCoSo method, hybrid model, transportation company selection
How to Cite
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. https://doi.org/10.3846/tede.2021.15058
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Lecture Notes in Economics and Mathematical Systems: Vol. 186. Multiple attribute decision making (pp. 58–191). Springer. https://doi.org/10.1007/978-3-642-48318-9_3
Ilgin, M. A., Gupta, S. M., & Battaïa, O. (2015). Use of MCDM techniques in environmentally conscious manufacturing and product recovery: State of the art. Journal of Manufacturing Systems, 37, 746–758. https://doi.org/10.1016/j.jmsy.2015.04.010
Jaukovic Jocic, K., Jocic, G., Karabasevic, D., Popovic, G., Stanujkic, D., Zavadskas, E. K., & Thanh Nguyen, P. (2020). A novel integrated PIPRECIA-interval-valued triangular fuzzy ARAS model: E-Learning course selection. Symmetry, 12(6), 928. https://doi.org/10.3390/sym12060928
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Karaşan, A., & Bolturk, E. (2019, August). Solid waste disposal site selection by using neutrosophic combined compromise solution method. In 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology (EUSFLAT 2019). Atlantis Press. https://doi.org/10.2991/eusflat-19.2019.58
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Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2018). An extended step-wise weight assessment ratio analysis with symmetric interval type-2 fuzzy sets for determining the subjective weights of criteria in multi-criteria decision-making problems. Symmetry, 10(4), 91. https://doi.org/10.3390/sym10040091
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Kundu, P., Kar, S., & Maiti, M. (2014). A fuzzy MCDM method and an application to solid transportation problem with mode preference. Soft Computing, 18(9), 1853–1864. https://doi.org/10.1007/s00500-013-1161-0
Liao, H., Xu, Z., Herrera-Viedma, E., & Herrera, F. (2018). Hesitant fuzzy linguistic term set and its application in decision making: A state-of-the-art survey. International Journal of Fuzzy Systems, 20(7), 2084–2110. https://doi.org/10.1007/s40815-017-0432-9
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