An evaluation of a third-party logistics provider: The application of the rough Dombi-Hamy mean operator
DOI:
https://doi.org/10.31181/dmame2003080fKeywords:
MADM; rough numbers; Rough Dombi-Hamy Mean Operator; third-party logistics.Abstract
Third-party logistics (3PL) has involved a significant response among researchers and practitioners in the recent decade. In the global competitive scenario, multinational companies (MNCs) not only improve the quality of the service and increase efficiency, but they also decrease costs by means of 3PL. However, the assessment and selection of 3PL is a very critical decision to make, comprising intricacy due to the existence of various imprecisely based criteria. Also, uncertainty is an unavoidable part of information in the decision-making process and its importance in the selection process is relatively high and needs to be carefully considered. Consequently, incomplete and inadequate data or information may occur among other various selection criteria, which can be termed as a multi-criteria decision-making (MCDM) problem. Rough numbers are very flexible to model this type of uncertainty occurring in MCDM problems. In this paper, the Hamy Mean (HM) operator and Dombi operations are expanded by rough numbers (RNs) so as to propose the Rough Number Dombi-Hamy Mean (RNDHM) operator. Then, the Multi-Attribute Decision-Making (MADM) model is designed with the RNDHM operator. Finally, the RNDHM is employed to achieve the final ranking of the 3PL providers.
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References
Atanassov, K. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87-96.
Azadi, M., & Saen, R.F. (2011). A new chance-constrained data envelopment analysis for selecting third-party reverse logistics providers in the existence of dual-role factors, Expert Systems with Applications, 38(10), 12231-12236.
Di Fazio, A., Bettinelli, D., Louette, E., Mechin, J.P., Zazza, M., Vecchiarelli, P., & Domanico, L. (2016). European Pathways to Introduce EGNOS and Galileo for Dangerous Goods Transport, Transportation Research Procedia, 14, 1482-1491.
Dombi, J. (1982). A general class of fuzzy operators, the demorgan class of fuzzy operators and fuzziness measures induced by fuzzy operators. Fuzzy Sets and Systems, 8, 149–163.
Hara, T., Uchiyama, M., & Takahasi, S.E. (1998). A refinement of various mean inequalities. Journal of Inequalities and Applications, 2(4), 387-395.
Jung, H. (2017). Evaluation of Third Party Logistics Providers Considering Social Sustainability, Sustainability, 9(5), 777.
Li, Z.X., Wei, G.W., & Lu, M. (2018). Pythagorean Fuzzy Hamy Mean Operators in Multiple Attribute Group Decision Making and Their Application to Supplier Selection. Symmetry, 10, 505.
Lien, N.T.K., & Day, J.-D. (2017). Estimating the efficiency of third party Logistics over the world, International Conference on System Science and Engineering, 621-624.
Ngan, S.C. (2017). A unified representation of intuitionistic fuzzy sets, hesitant fuzzy sets and generalized hesitant fuzzy sets based on their u-maps. Expert Systems With Applications, 69, 257–276.
Nguyen, H. (2016). A new interval-value d knowledge measure for interval-valued intuitionistic fuzzy sets and application in decision making. Expert Systems With Applications, 56, 143–155.
Pamučar, D., Mihajlović, M., Obradović, R., & Atanasković, P. (2017). Novel approach to group multi-criteria decision making based on interval rough numbers: Hybrid DEMATEL-ANP-MAIRCA model, Expert Systems with Applications, 88, 58-80.
Pamučar, D., Petrović, I., & Ćirović, G. (2018). Modification of the Best-Worst and MABAC methods: A novel approach based on interval-valued fuzzy-rough numbers, Expert Systems with Applications, 91, 89-106.
Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data (Vol. 9). Berlin: Springer.
Sizong, G., & Tao, S. (2016). Interval-Valued Fuzzy Number and Its Expression Based on Structured Element. Advances in Intelligent and Soft Computing, 62, 1417-1425.
Song, W., Ming, X., Han, Y., & Wu, Z. (2013). A rough set approach for evaluating vague customer requirement of industrial product service system. International Journal of Production Research, 51(22), 6681–6701.
Sremac, S., Stevic, Z., Pamucar D, Arsić, M., & Matić, B. (2018). Evaluation of a Third-Party Logistics (3PL) Provider Using a Rough SWARA–WASPAS Model Based on a New Rough Dombi Agregator. Symmetry, 10(8), 305.
Stevic, Z., Pamucar D, Subotic, M., Antucheviciene, J., & Zavadskas, E.K. (2018). The location selection for roundabout construction using Rough BWM -Rough WASPAS approach based on a new Rough Hamy aggregator. Sustainability, 10(8), 2817.
Tanackov, I., Janković, Z., Sremac, S., Miličić, M., Vasiljević, M., Mihaljev-Martinov, J., & Škiljaica I. (2018). Risk distribution of dangerous goods in logistics subsystems, Journal of Loss Prevention in the Process Industries, 54, 373-383.
Zadeh, L.A. (1965). Fuzzy sets, Information and Control, 8(3), 338-353.
Zywica, P., Stachowiak, A., & Wygralak, M. (2016). An algorithmic study of relative cardinalities for interval-valued fuzzy sets. Fuzzy Sets and Systems, 294, 105–124.
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