Selection of a location for the development of multimodal logistics center: Application of single-valued neutrosophic MABAC model

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.

[1]  Yusuf Subas,et al.  A ranking method of single valued neutrosophic numbers and its applications to multi-attribute decision making problems , 2016, International Journal of Machine Learning and Cybernetics.

[2]  Tzu-Liang Tseng,et al.  A hybrid algorithm for capacitated plant location problem , 2010, Expert Syst. Appl..

[3]  R. J. Kuo,et al.  A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network , 2002, Comput. Ind..

[4]  K. Atanassov,et al.  Interval-Valued Intuitionistic Fuzzy Sets , 2019, Studies in Fuzziness and Soft Computing.

[5]  I. C. Ogwude,et al.  An Analytic Hierarchy Process (AHP) Approach to Port Selection Decisions – Empirical Evidence from Nigerian Ports , 2006 .

[6]  Yahia Zare Mehrjerdi,et al.  Using greedy clustering method to solve capacitated location-routing problem with fuzzy demands , 2013, Eur. J. Oper. Res..

[7]  Zhenhua Wei,et al.  Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network , 2009 .

[8]  Goran Ćirović,et al.  New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods , 2019, Neural Computing and Applications.

[9]  Slobodan M. Zecevic Robni terminali i robno-transportni centri , 2006 .

[10]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets , 1986 .

[11]  Florentin Smarandache,et al.  Subtraction and Division of Neutrosophic Numbers , 2017 .

[12]  Elyas Shivanian,et al.  AN EXTENDED METHOD USING TOPSIS AND VIKOR FOR MULTIPLE ATTRIBUTE DECISION MAKING WITH MULTIPLE DECISION MAKERS AND SINGLE VALUED NEUTROSOPHIC NUMBERS , 2017 .

[13]  Surapati Pramanik,et al.  TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment , 2014, Neural Computing and Applications.

[14]  Romualdas Bausys,et al.  Sustainable Assessment of Alternative Sites for the Construction of a Waste Incineration Plant by Applying WASPAS Method with Single-Valued Neutrosophic Set , 2015 .

[15]  Edmundas Kazimieras Zavadskas,et al.  Greenhouse locating based on ANP-COPRAS-G methods – an empirical study based on Iran , 2012 .

[16]  Chen Si-yun Factors and a Method of Selecting a Site for a Logistical Centre , 2006 .

[17]  Xindong Peng,et al.  Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function , 2018, Neural Computing and Applications.

[18]  Peide Liu,et al.  The Evaluation Study on Location Selection of Logistics Center Based on Fuzzy AHP and TOPSIS , 2007, 2007 International Conference on Wireless Communications, Networking and Mobile Computing.

[19]  Jiaqin Yang,et al.  AN AHP DECISION MODEL FOR FACILITY LOCATION SELECTION , 1997 .

[20]  Vinh V. Thai,et al.  Selecting the location of distribution centre in logistics operations: A conceptual framework and case study , 2005 .

[21]  Jun Ye,et al.  Multicriteria decision-making method using the correlation coefficient under single-valued neutrosophic environment , 2013, Int. J. Gen. Syst..

[22]  Ali Azadeh,et al.  Behavioral simulation and optimization of generation companies in electricity markets by fuzzy cognitive map , 2012, Expert Syst. Appl..

[23]  Ragheb Rahmaniani,et al.  Robust Capacitated Facility Location Problem: Optimization Model and Solution Algorithms , 2013 .

[24]  Minghe Sun,et al.  A tabu search heuristic procedure for the capacitated facility location problem , 2012, J. Heuristics.

[25]  Yanqing Zhang,et al.  Interval Neutrosophic Sets and Logic: Theory and Applications in Computing , 2005, ArXiv.

[26]  Zhang-peng Tian,et al.  Hybrid single-valued neutrosophic MCGDM with QFD for market segment evaluation and selection , 2018, J. Intell. Fuzzy Syst..

[27]  Reza Zanjirani Farahani,et al.  Combination of MCDM and covering techniques in a hierarchical model for facility location: A case study , 2007, Eur. J. Oper. Res..

[28]  Luis Ferreira,et al.  Multi-objective evaluation of intermodal freight terminal location decisions , 2005 .

[29]  Mariano Jiménez,et al.  PROMETHEE: an extension through fuzzy mathematical programming , 2005, J. Oper. Res. Soc..

[30]  Shuo-Yan Chou,et al.  International distribution center selection from a foreign market perspective using a weighted fuzzy factor rating system , 2009, Expert Syst. Appl..

[31]  Luis Ferreira,et al.  Modeling Intermodal Freight Hub Location Decisions , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[32]  Reza Tavakkoli-Moghaddam,et al.  A HOLISTIC APPROACH BASED ON MCDM FOR SOLVING LOCATION PROBLEMS , 2007 .