Selection of a location for the development of multimodal logistics center: Application of single-valued neutrosophic MABAC model
DOI:
https://doi.org/10.31181/oresta1902039pKeywords:
single-valued neutrosophic sets, MABAC, logistics center, multi-criteria decision making.Abstract
A logistics center (LC) is unique technological, spatial, organizational and economic unity that brings together different providers and users of logistics services. By selecting the optimal LC location, transport costs are reduced and business performance, competitiveness and profitability are improved. In order to achieve the overall optimum, it is necessary to perform adequate evaluation and selection of the optimal location for the construction of a LC. In this paper is performed the evaluation of potential locations based on new approach in the field of logistics. Weight coefficients of criteria are determined using objective model integrated in Single-Valued Neutrosophic (SVNN) Multi-Attributive Border Approximation Area Comparison (MABAC) model. In order to determine the stability of the model, the SVNN MABAC model is compared with other representative multi-criteria models. In the final part of the model validation, statistical correlation between the SVNN MABAC model and other proposed approaches (SVNN WASPAS, SVNN VIKOR, SVNN TOPSIS and SVNN CODAS) is performed.
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References
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Yang, J., & Lee, H. (1997). An AHP decision model for facility location selection. Facilities, 15(9/10), 241-254.
Ye, J. (2013a). Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. International Journal of General Systems, 42(4), 386–394.
Zadeh, L. A. (1965). Fuzzy sets, Information and Control, 8(3), 338–353.
Zare Mehrjerdi, Y., & Nadizadeh, A. (2013). Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands. European Journal of Operational Research, 229(1), 75–84.
Zavadskas, E.K., Baušys, R., Lazauskas, M. (2015). Sustainable Assessment of Alternative Sites for the Construction of a Waste Incineration Plant by Applying WASPAS Method with Single-Valued Neutrosophic Set, Sustainability, 7, 15923–15936; doi:10.3390/su71215792.
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Atanassov, K. T. (1986). Intuitionistic fuzzy sets, Fuzzy Sets and Systems, vol. 20, no. 1, pp. 87–96.
Atanassov, K. T. and Gargov, G. (1989). Interval valued Intuitionistic fuzzy sets, Fuzzy Sets and Systems, vol. 31, no. 3, pp. 343–349.
Biswas, P., Pramanik, S., Giri, C.B. (2016). TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Computing and Applications, 27, 727–737. doi: 10.1007/s00521-015-1891-2.
Cao, Y. (2009). The improvement of logistics center addressing algorithm and the design of its Hopfield neural network. Computer Applications and Software. 3(26): 117-120.
Chen S., & Liu, X. (2006). Factors and a Method of Selecting a Site for a Logistical Centre. Journal of Weinan Teachers College, 3.
Chuang, P. T. (2002). A QFD approach for distributions location model. International Journal of Quality & Reliability Management, 19(8/9), 1037-1054.
Deli, I., Şubaş, Y. (2017). A ranking method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems. International Journal of Machine Learning and Cybernetics, 8, 1309–1322. doi: 10.1007/s13042-016-0505-3.
Farahani, R. Z., & Asgari, N. (2007). Combination of MCDM and covering techniques in a hierarchical model for facility location: A case study. European Journal of Operational Research, 176(3), 1839-1858.
Fernández-Castro, A.S., Jiménez, M., 2005. PROMETHEE: An extension through fuzzy mathematical programming. Journal of the Operational Research Society 56, 119–122.
Ghaderi, S.F., Azadeh, A., Nokhandan, B.P., Fathi, E. (2012). Behavioral simulation and optimization of generation companies in electrical markets by fuzzy cognitive map. Expert systems with applications, 39, 4635–4646.
Ghoseiri, K., & Lessan, J. (2008). Location selection for logistic centres using a two-step fuzzy AHP and ELECTE method. Proceedings of the 9th Asia Pasific Industrial Engineering & Management Systems Conference, Indonesia, 434-440.
Kaboli, A., Aryanezhad, M. B., & Shahanaghi, K. (2007). A holistic approach based on MCDM for solving location problems. International Journal of Engineering Transactions A: Basics, 20(3), 252-262.
Kannan G., Noorul Haq, P., & Sasikumar, P. (2008). An application of the Analytic Hierarchy Process and Fuzzy Analytic Hierarchy Process in the selection of collecting centre location for the reverse logistics Multi-criteria Decision-Making supply chain model. International Journal of Management and Decision Making, 9(4), 350-365.
Kuo, R. J., Chi, S. C., & Kao, S. S. (2002). A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network. Computers in Industry, 47(2), 199-214.
Lai, M. C., Sohn, H. S., Tseng, T. L., & Chiang, C. (2010). A hybrid algorithm for capacitated plant location problem. Expert Systems with Applications, 37(12), 8599–8605 .
Ou, C.-W., & Chou, S.-Y. (2009). International distribution centre selection from a foreign market perspective using a weighted fuzzy factor rating system. Expert System with Applications, 36(2), 1773-1782.
Pamucar, D., Badi, I., Korica, S., Obradović, R. (2018). A novel approach for the selection of power generation technology using an linguistic neutrosophic combinative distance-based assessment (CODAS) method: A case study in Libya. Energies, 11(9), 2489
Pamucar, D., Sremac, S., Stević, Ž., Ćirović, G., Tomić, D. (2019). New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods. Neural Computing and Applications. https://10.1007/s00521-018-03997-7
Peng, X., & Dai, J. (2018). Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Computing and Applications, 29(10) 939-954.
Pouresmaeil H., Shivanian E., Khorram E., Fathabadi H.S. (2017). An extended method using TOPSIS and VIKOR for multiple attribute decision making with multiple decision makers and single valued neutrosophic numbers. Advances and Applications in Statistics, 50, 261–292.
Rahmaniani, R., Saidi-Mehrabad, M., & Ashouri, H. (2013). Robust capacitated facility location problem optimization model and solution algorithms. Journal of Uncertain Systems, 7(1), 22–35.
Rezaeiniya, N., Zolfani, S. H., & Zavadskas, E. K. (2012). Greenhouse locating based on ANP-COPRAS-G methods - an empirical study based on Iran. International Journal of Strategic Property Management, 16(2), 188–200.
Shao, Y., Chen, Q., Wei, Z. (2009). Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network, Communications in Computer and Information Science Volume 51, pp 305-312.
Sirikijpanichkul, A., & Ferreira, L. (2005). Multi-objective evaluation of intermodal freight terminal location decisions. Proceedings of the 27th Conference of Australian Institute of Transport Research (CAITR), Queensland University of Tech, 7-9 December 2005.
Sirikijpanichkul, A., & Ferreira, L. (2006). Modeling intermodal freight hub location decisions. 2006 IEEE International Conference on Systems,Man and Cybernetics, Oct. 8-11, Taipei, Taiwan.
Smarandache, F (1999). A unifying field in logics. Neutrosophy: neutrosophic probability, set and logic. American Research Press, Rehoboth.
Smarandache, F. (2005). A generalization of the Intuitionistic fuzzy set. International journal of Pure and Applied Mathematics, 24, 287-297.
Smarandache, F. (2016). Subtraction and division of neutrosophic numbers. Critical Review, 13, 103–110.
Sun, M. (2012). A tabu search heuristic procedure for the capacitated facility location problem. Journal of Heuristics, 18(1), 91–118.
Tian, Z. P., Wang, J. Q., & Zhang, H. Y. (2018). Hybrid single-valued neutrosophic MCGDM with QFD for market segment evaluation and selection. Journal of Intelligent & Fuzzy Systems, 34(1), 177-187.
Ugboma, C., Ugboma, O., & Ogwude, I. (2006). An Analytic Hierarchy Process (AHP) approach to Port selection decisions –empirical evidence from Nigerian Ports. Maritime Economics & Logistics, 8, 251–266.
Vinh Van, T., & Devinder, G. (2005). Selecting the location of distribution centre in logistics operations: A conceptual framework and case study. Asia Pacific Journal of Marketing and Logistics, 17(3), 3-24.
Wang, H., Smarandache, F., Zhang, Y. Q. and Sunderraman. (2005). Interval neutrosophic sets and logic: Theory and applications in computing, Hexis, Phoenix, AZ.
Wang, H., Smarandache, F., Zhang, Y. Q., and Sunderraman, R., (2010). Single valued neutrosophic sets, Multispace and Multistructure (4) 410-413.
Wang, S., & Liu, P. (2007). The evaluation study on location selection of logistics centre based on fuzzy AHP and TOPSIS. International Conference on Wireless Communications, Networking and Mobile Computing, 21-25.09.2007, 3779 – 3782.
Yang, J., & Lee, H. (1997). An AHP decision model for facility location selection. Facilities, 15(9/10), 241-254.
Ye, J. (2013a). Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment. International Journal of General Systems, 42(4), 386–394.
Zadeh, L. A. (1965). Fuzzy sets, Information and Control, 8(3), 338–353.
Zare Mehrjerdi, Y., & Nadizadeh, A. (2013). Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands. European Journal of Operational Research, 229(1), 75–84.
Zavadskas, E.K., Baušys, R., Lazauskas, M. (2015). Sustainable Assessment of Alternative Sites for the Construction of a Waste Incineration Plant by Applying WASPAS Method with Single-Valued Neutrosophic Set, Sustainability, 7, 15923–15936; doi:10.3390/su71215792.
Zecevic, S. (2006). Robni terminali i robno-transportni centri. Saobraćajni fakultet Univerziteta u Begradu.
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Published
2019-08-19
How to Cite
Pamučar, D., & Božanić, D. (2019). Selection of a location for the development of multimodal logistics center: Application of single-valued neutrosophic MABAC model. Operational Research in Engineering Sciences: Theory and Applications, 2(2), 55–71. https://doi.org/10.31181/oresta1902039p
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