A new decision model for cross-docking center location in logistics networks under interval-valued intuitionistic fuzzy uncertainty
Abstract
Cross-dock has been a novel logistic approach to effectively consolidate and distribute multiple products in logistics networks. Location selection of cross-docking centers is a decision problem under different conflicting criteria. The decision has a vital part in the strategic design of distribution networks in logistics management. Conventional methods for the location selection of cross-docking centers are insufficient for handling uncertainties in Decision-Makers (DMs) or experts’ opinions. This study presents a modern Multi-Criteria Group Decision-Making (MCGDM) model, which applies the concept of compromise solution under uncertainty. To address uncertainty, Interval-Valued Intuitionistic Fuzzy (IVIF) sets are used. In this paper, first an IVIF-weighted arithmetic averaging (IVIF-WAA) operator is used in order to aggregate all IVIF-decision matrices, which were made by a team of the DMs into final IVIF-decision matrix. Then, a new Collective Index (CI) is developed that simultaneously regards distances of cross-docking centers as candidates from the IVIF-ideal points. Finally, the feasibility and practicability of proposed MCGDM model is illustrated with an application example on location choices of cross-docking centers to the logistics network design.
Keyword : multiple cross-docks, location evaluation and selection, logistics networks, multi-criteria decision-making, group decision process, interval-valued intuitionistic fuzzy sets
How to Cite
Mousavi, S. M., Antuchevičienė, J., Zavadskas, E. K., Vahdani, B., & Hashemi, H. (2019). A new decision model for cross-docking center location in logistics networks under interval-valued intuitionistic fuzzy uncertainty. Transport, 34(1), 30-40. https://doi.org/10.3846/transport.2019.7442
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Atanassov, K. T. 1994. Operators over interval valued intuitionistic fuzzy sets, Fuzzy Sets and Systems 64(2): 159–174. https://doi.org/10.1016/0165-0114(94)90331-X
Atanassov, K. T.; Gargov, G. 1989. Interval valued intuitionistic fuzzy sets, Fuzzy Sets and Systems 31(3): 343–349. https://doi.org/10.1016/0165-0114(89)90205-4
Bartholdi, J. J.; Gue, K. R. 2004. The best shape for a crossdock, Transportation Science 38(2): 235–244. https://doi.org/10.1287/trsc.1030.0077
Chen, C.-T. 2000. Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets and Systems 114(1): 1–9. https://doi.org/10.1016/S0165-0114(97)00377-1
Chen, S.-J.; Hwang, C.-L. 1992. Fuzzy Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag Berlin Heidelberg. 536 p. https://doi.org/10.1007/978-3-642-46768-4
Chen, T.-Y. 2016. An IVIF-ELECTRE outranking method for multiple criteria decision-making with interval-valued intuitionistic fuzzy sets, Technological and Economical Development of Economy 23(3): 416–452. https://doi.org/10.3846/20294913.2015.1072751
Chen, T.-Y. 2015. IVIF-PROMETHEE outranking methods for multiple criteria decision analysis based on interval-valued intuitionistic fuzzy sets, Fuzzy Optimization and Decision Making 14(2): 173–198. https://doi.org/10.1007/s10700-014-9195-z
Demirel, T.; Demirel, N. Ç.; Kahraman, C. 2010. Multi-criteria warehouse location selection using Choquet integral, Expert Systems with Applications 37(5): 3943–3952. https://doi.org/10.1016/j.eswa.2009.11.022
Hashemi, H.; Bazargan, J.; Mousavi, S. M.; Vahdani, B. 2014. An extended compromise ratio model with an application to reservoir flood control operation under an interval-valued intuitionistic fuzzy environment, Applied Mathematical Modelling 38(14): 3495–3511. https://doi.org/10.1016/j.apm.2013.11.045
Hashemi, S. S.; Razavi Hajiagha, S. H.; Zavadskas, E. K.; Mahdiraji, H. A. 2016. Multicriteria group decision making with ELECTRE III method based on interval-valued intuitionistic fuzzy information, Applied Mathematical Modelling 40(2): 1554–1564. https://doi.org/10.1016/j.apm.2015.08.011
Hwang, C.-L.; Yoon, K. 1981. Multiple Attribute Decision Making. Methods and Applications: a State-of-the-Art Survey. Springer-Verlag Berlin Heidelberg. 269 p. https://doi.org/10.1007/978-3-642-48318-9
Jayaraman, V.; Ross, A. 2003. A simulated annealing methodology to distribution network design and management, European Journal of Operational Research 144(3): 629–645. https://doi.org/10.1016/S0377-2217(02)00153-4
Kahraman, C.; Keshavarz Ghorabaee, M.; Zavadskas, E. K.; Onar, S.C; Yazdani, M.; Oztaysi, B. 2017. Intuitionistic fuzzy EDAS method: an application to solid waste disposal site selection, Journal of Environmental Engineering and Landscape Management 25(1): 1–12. https://doi.org/10.3846/16486897.2017.1281139
Kellar, G. M.; Polak, G. G.; Zhang, X. 2016. Synchronization, cross-docking, and decoupling in supply chain networks, International Journal of Production Research 54(9): 2585–2599. https://doi.org/10.1080/00207543.2015.1107195
Khalaj, M. R.; Modarres, M.; Tavakkoli-Moghaddam, R. 2014. Designing a multi-echelon supply chain network: a car manufacturer case study, Journal of Intelligent & Fuzzy Systems 27(6): 2897–2914. https://doi.org/10.3233/IFS-141250
Ladier, A.-L.; Alpan, G. 2016. Cross-docking operations: current research versus industry practice, Omega 62: 145–162. https://doi.org/10.1016/j.omega.2015.09.006
Lee, H.-S. 2005. A fuzzy multi-criteria decision making model for the selection of the distribution center, Lecture Notes in Computer Science 3612: 1290–1299. https://doi.org/10.1007/11539902_164
Li, Y.; Liu, P.; Chen, Y. 2016. Some single valued neutrosophic number Heronian mean operators and their application in multiple attribute group decision making, Informatica 27(1): 85–110. https://doi.org/10.15388/Informatica.2016.78
Liu, P. 2016. Special issue “Intuitionistic fuzzy theory and its application in economy, technology and management”, Technological and Economic Development of Economy 22(3): 327–335. https://doi.org/10.3846/20294913.2016.1185047
Liu, P.; Li, Y.; Antuchevičienė, J. 2016. Multi-criteria decision-making method based on intuitionistic trapezoidal fuzzy prioritised OWA operator, Technological and Economic Development of Economy 22(3): 453–469. https://doi.org/10.3846/20294913.2016.1171262
Maknoon, M. Y.; Soumis, F.; Baptiste, P. 2016. Optimizing transshipment workloads in less-than-truckload cross-docks, International Journal of Production Economics 179: 90–100. https://doi.org/10.1016/j.ijpe.2016.05.015
Makui, A.; Haerian, L.; Eftekhar, M. 2006. Designing a multiobjective nonlinear cross-docking location allocation model using genetic algorithm, Journal of Industrial Engineering International 2(3): 27–42.
Mousavi, S. M.; Tavakkoli-Moghaddam, R.; Siadat, A. 2014a. Optimally design of the cross-docking in distribution networks: heuristic solution approach, International Journal of Engineering: Transactions A: Basics 27(4): 533–544.
Mousavi, S. M.; Vahdani, B. 2017. A robust approach to multiple vehicle location-routing problems with time windows for optimization of cross-docking under uncertainty, Journal of Intelligent & Fuzzy Systems 32(1): 49–62. https://doi.org/10.3233/JIFS-151050
Mousavi, S. M.; Vahdani, B.; Tavakkoli-Moghaddam, R.; Hashemi, H. 2014b. Location of cross-docking centers and vehicle routing scheduling under uncertainty: a fuzzy possibilistic-stochastic programming model, Applied Mathematical Modelling 38(7–8): 2249–2264. https://doi.org/10.1016/j.apm.2013.10.029
Ou, C.-W.; Chou, S.-Y. 2009. International distribution center selection from a foreign market perspective using a weighted fuzzy factor rating system, Expert Systems with Applications 36(2): 1773–1782. https://doi.org/10.1016/j.eswa.2007.12.007
Özcan, T.; Çelebi, N.; Esnaf, Ş. 2011. Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem, Expert Systems with Applications 38(8): 9773–9779. https://doi.org/10.1016/j.eswa.2011.02.022
Park, J. H.; Park, I. Y.; Kwun, Y. C.; Tan, X. 2011. Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment, Applied Mathematical Modelling 35(5): 2544–2556. https://doi.org/10.1016/j.apm.2010.11.025
Prentkovskis, O.; Erceg, Ž.; Stević, Ž.; Tanackov, I.; Vasiljević, M.; Gavranović, M. 2018. A new methodology for improving service quality measurement: Delphi-FUCOM-SERVQUAL model, Symmetry 10(12): 757. https://doi.org/10.3390/sym10120757
Ratliff, H. D.; Donadlson, H.; Johnson, E.; Zhang, M. 1998. Schedule-Driven Cross-Docking Network. Technical Report. Georgia Institute of Technology, GA, US.
Razavi Hajiagha, S. H.; Hashemi, S. S.; Zavadskas, E. K. 2013. A complex proportional assessment method for group decision making in an interval-valued intuitionistic fuzzy environment, Technological and Economical Development of Economy 19(1): 22–37. https://doi.org/10.3846/20294913.2012.762953
Razavi Hajiagha, S. H.; Mandiraji, H. A.; Hashemi, S. S.; Zavadskas, E. K. 2015. Evolving a linear programming technique for MAGDM problems with interval valued intuitionistic fuzzy information, Expert Systems with Applications 42(23): 9318–9325. https://doi.org/10.1016/j.eswa.2015.07.067
Rong, L.; Liu, P.; Chu, Y. 2016. Multiple attribute group decision making methods based on intuitionistic fuzzy generalized Hamacher aggregation operator, Economic Computation and Economic Cybernetics Studies and Research 50(2): 211–230.
Ross, A.; Jayaraman, V. 2008. An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design, Computers & Industrial Engineering 55(1): 64–79. https://doi.org/10.1016/j.cie.2007.12.001
Stević, Ž.; Pamučar, D.; Zavadskas, E. K.; Ćirović, G.; Prentkovskis, O. 2017. The selection of wagons for the internal transport of a logistics company: a novel approach based on rough BWM and rough SAW methods, Symmetry 9(11): 264. https://doi.org/10.3390/sym9110264
Vlachopoulou, M.; Silleos, G.; Manthou, V. 2001. Geographic information systems in warehouse site selection decisions, International Journal of Production Economics 71(1–3): 205–212. https://doi.org/10.1016/S0925-5273(00)00119-5
Wei, G.; Wang, X. 2007. Some geometric aggregation operators based on interval-valued intuitionistic fuzzy sets and their application to group decision making, in 2007 International Conference on Computational Intelligence and Security (CIS 2007), 15–19 December 2007, Harbin, China, 495–499. https://doi.org/10.1109/CIS.2007.84
Xu, Z. 2007a. Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making, Control and Decision 22(2): 215–219.
Xu, Z. 2007b. Multi-person multi-attribute decision making models under intuitionistic fuzzy environment, Fuzzy Opti mization and Decision Making 6(3): 221–236. https://doi.org/10.1007/s10700-007-9009-7
Xu, Z.-S.; Chen, J. 2007. Approach to group decision making based on interval-valued intuitionistic judgment matrices, Systems Engineering – Theory & Practice 27(4): 126–133. https://doi.org/10.1016/S1874-8651(08)60026-5
Xue, Y.-X.; You, J.-X.; Lai, X.-D.; Liu, H.-C. 2016. An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information, Applied Soft Computing 38: 703–713. https://doi.org/10.1016/j.asoc.2015.10.010
Yan, H.; Tang, S.-L. 2009. Pre-distribution and post-distribution cross-docking operations, Transportation Research Part E: Logistics and Transportation Review 45(6): 843–859. https://doi.org/10.1016/j.tre.2009.05.005
Yu, D.; Wu, Y.; Lu, T. 2012. Interval-valued intuitionistic fuzzy prioritized operators and their application in group decision making, Knowledge-Based Systems 30: 57–66. https://doi.org/10.1016/j.knosys.2011.11.004
Zadeh, L. A. 1965. Fuzzy sets, Information and Control 8(3): 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zavadskas, E. K.; Antuchevičienė, J.; Razavi Hajiagha, S. H.; Hashemi, S. S. 2014. Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF), Applied Soft Computing 24: 1013–1021. https://doi.org/10.1016/j.asoc.2014.08.031
Zavadskas, E. K.; Antuchevičienė, J.; Razavi Hajiagha, S. H.; Hashemi, S. S. 2015. The interval-valued intuitionistic fuzzy MULTIMOORA method for group decision making in engineering, Mathematical Problems in Engineering 2015: 560690. https://doi.org/10.1155/2015/560690
Zavadskas, E. K.; Nunić, Z.; Stjepanović, Ž.; Prentkovskis, O. 2018a. A novel rough range of value method (R-ROV) for selecting automatically guided vehicles (AGVs), Studies in Informatics and Control 27(4): 385–394. https://doi.org/10.24846/v27i4y201802
Zavadskas, E. K.; Stević, Ž.; Tanackov, I.; Prentkovskis, O. 2018b. A novel multicriteria approach – rough step-wise weight assessment ratio analysis method (R-SWARA) and its application in logistics, Studies in Informatics and Control 27(1): 97–106. https://doi.org/10.24846/v27i1y201810
Atanassov, K. T. 1986. Intuitionistic fuzzy sets, Fuzzy Sets and Systems 20(1): 87–96. https://doi.org/10.1016/S0165-0114(86)80034-3
Atanassov, K. T. 1994. Operators over interval valued intuitionistic fuzzy sets, Fuzzy Sets and Systems 64(2): 159–174. https://doi.org/10.1016/0165-0114(94)90331-X
Atanassov, K. T.; Gargov, G. 1989. Interval valued intuitionistic fuzzy sets, Fuzzy Sets and Systems 31(3): 343–349. https://doi.org/10.1016/0165-0114(89)90205-4
Bartholdi, J. J.; Gue, K. R. 2004. The best shape for a crossdock, Transportation Science 38(2): 235–244. https://doi.org/10.1287/trsc.1030.0077
Chen, C.-T. 2000. Extensions of the TOPSIS for group decision-making under fuzzy environment, Fuzzy Sets and Systems 114(1): 1–9. https://doi.org/10.1016/S0165-0114(97)00377-1
Chen, S.-J.; Hwang, C.-L. 1992. Fuzzy Multiple Attribute Decision Making: Methods and Applications. Springer-Verlag Berlin Heidelberg. 536 p. https://doi.org/10.1007/978-3-642-46768-4
Chen, T.-Y. 2016. An IVIF-ELECTRE outranking method for multiple criteria decision-making with interval-valued intuitionistic fuzzy sets, Technological and Economical Development of Economy 23(3): 416–452. https://doi.org/10.3846/20294913.2015.1072751
Chen, T.-Y. 2015. IVIF-PROMETHEE outranking methods for multiple criteria decision analysis based on interval-valued intuitionistic fuzzy sets, Fuzzy Optimization and Decision Making 14(2): 173–198. https://doi.org/10.1007/s10700-014-9195-z
Demirel, T.; Demirel, N. Ç.; Kahraman, C. 2010. Multi-criteria warehouse location selection using Choquet integral, Expert Systems with Applications 37(5): 3943–3952. https://doi.org/10.1016/j.eswa.2009.11.022
Hashemi, H.; Bazargan, J.; Mousavi, S. M.; Vahdani, B. 2014. An extended compromise ratio model with an application to reservoir flood control operation under an interval-valued intuitionistic fuzzy environment, Applied Mathematical Modelling 38(14): 3495–3511. https://doi.org/10.1016/j.apm.2013.11.045
Hashemi, S. S.; Razavi Hajiagha, S. H.; Zavadskas, E. K.; Mahdiraji, H. A. 2016. Multicriteria group decision making with ELECTRE III method based on interval-valued intuitionistic fuzzy information, Applied Mathematical Modelling 40(2): 1554–1564. https://doi.org/10.1016/j.apm.2015.08.011
Hwang, C.-L.; Yoon, K. 1981. Multiple Attribute Decision Making. Methods and Applications: a State-of-the-Art Survey. Springer-Verlag Berlin Heidelberg. 269 p. https://doi.org/10.1007/978-3-642-48318-9
Jayaraman, V.; Ross, A. 2003. A simulated annealing methodology to distribution network design and management, European Journal of Operational Research 144(3): 629–645. https://doi.org/10.1016/S0377-2217(02)00153-4
Kahraman, C.; Keshavarz Ghorabaee, M.; Zavadskas, E. K.; Onar, S.C; Yazdani, M.; Oztaysi, B. 2017. Intuitionistic fuzzy EDAS method: an application to solid waste disposal site selection, Journal of Environmental Engineering and Landscape Management 25(1): 1–12. https://doi.org/10.3846/16486897.2017.1281139
Kellar, G. M.; Polak, G. G.; Zhang, X. 2016. Synchronization, cross-docking, and decoupling in supply chain networks, International Journal of Production Research 54(9): 2585–2599. https://doi.org/10.1080/00207543.2015.1107195
Khalaj, M. R.; Modarres, M.; Tavakkoli-Moghaddam, R. 2014. Designing a multi-echelon supply chain network: a car manufacturer case study, Journal of Intelligent & Fuzzy Systems 27(6): 2897–2914. https://doi.org/10.3233/IFS-141250
Ladier, A.-L.; Alpan, G. 2016. Cross-docking operations: current research versus industry practice, Omega 62: 145–162. https://doi.org/10.1016/j.omega.2015.09.006
Lee, H.-S. 2005. A fuzzy multi-criteria decision making model for the selection of the distribution center, Lecture Notes in Computer Science 3612: 1290–1299. https://doi.org/10.1007/11539902_164
Li, Y.; Liu, P.; Chen, Y. 2016. Some single valued neutrosophic number Heronian mean operators and their application in multiple attribute group decision making, Informatica 27(1): 85–110. https://doi.org/10.15388/Informatica.2016.78
Liu, P. 2016. Special issue “Intuitionistic fuzzy theory and its application in economy, technology and management”, Technological and Economic Development of Economy 22(3): 327–335. https://doi.org/10.3846/20294913.2016.1185047
Liu, P.; Li, Y.; Antuchevičienė, J. 2016. Multi-criteria decision-making method based on intuitionistic trapezoidal fuzzy prioritised OWA operator, Technological and Economic Development of Economy 22(3): 453–469. https://doi.org/10.3846/20294913.2016.1171262
Maknoon, M. Y.; Soumis, F.; Baptiste, P. 2016. Optimizing transshipment workloads in less-than-truckload cross-docks, International Journal of Production Economics 179: 90–100. https://doi.org/10.1016/j.ijpe.2016.05.015
Makui, A.; Haerian, L.; Eftekhar, M. 2006. Designing a multiobjective nonlinear cross-docking location allocation model using genetic algorithm, Journal of Industrial Engineering International 2(3): 27–42.
Mousavi, S. M.; Tavakkoli-Moghaddam, R.; Siadat, A. 2014a. Optimally design of the cross-docking in distribution networks: heuristic solution approach, International Journal of Engineering: Transactions A: Basics 27(4): 533–544.
Mousavi, S. M.; Vahdani, B. 2017. A robust approach to multiple vehicle location-routing problems with time windows for optimization of cross-docking under uncertainty, Journal of Intelligent & Fuzzy Systems 32(1): 49–62. https://doi.org/10.3233/JIFS-151050
Mousavi, S. M.; Vahdani, B.; Tavakkoli-Moghaddam, R.; Hashemi, H. 2014b. Location of cross-docking centers and vehicle routing scheduling under uncertainty: a fuzzy possibilistic-stochastic programming model, Applied Mathematical Modelling 38(7–8): 2249–2264. https://doi.org/10.1016/j.apm.2013.10.029
Ou, C.-W.; Chou, S.-Y. 2009. International distribution center selection from a foreign market perspective using a weighted fuzzy factor rating system, Expert Systems with Applications 36(2): 1773–1782. https://doi.org/10.1016/j.eswa.2007.12.007
Özcan, T.; Çelebi, N.; Esnaf, Ş. 2011. Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem, Expert Systems with Applications 38(8): 9773–9779. https://doi.org/10.1016/j.eswa.2011.02.022
Park, J. H.; Park, I. Y.; Kwun, Y. C.; Tan, X. 2011. Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment, Applied Mathematical Modelling 35(5): 2544–2556. https://doi.org/10.1016/j.apm.2010.11.025
Prentkovskis, O.; Erceg, Ž.; Stević, Ž.; Tanackov, I.; Vasiljević, M.; Gavranović, M. 2018. A new methodology for improving service quality measurement: Delphi-FUCOM-SERVQUAL model, Symmetry 10(12): 757. https://doi.org/10.3390/sym10120757
Ratliff, H. D.; Donadlson, H.; Johnson, E.; Zhang, M. 1998. Schedule-Driven Cross-Docking Network. Technical Report. Georgia Institute of Technology, GA, US.
Razavi Hajiagha, S. H.; Hashemi, S. S.; Zavadskas, E. K. 2013. A complex proportional assessment method for group decision making in an interval-valued intuitionistic fuzzy environment, Technological and Economical Development of Economy 19(1): 22–37. https://doi.org/10.3846/20294913.2012.762953
Razavi Hajiagha, S. H.; Mandiraji, H. A.; Hashemi, S. S.; Zavadskas, E. K. 2015. Evolving a linear programming technique for MAGDM problems with interval valued intuitionistic fuzzy information, Expert Systems with Applications 42(23): 9318–9325. https://doi.org/10.1016/j.eswa.2015.07.067
Rong, L.; Liu, P.; Chu, Y. 2016. Multiple attribute group decision making methods based on intuitionistic fuzzy generalized Hamacher aggregation operator, Economic Computation and Economic Cybernetics Studies and Research 50(2): 211–230.
Ross, A.; Jayaraman, V. 2008. An evaluation of new heuristics for the location of cross-docks distribution centers in supply chain network design, Computers & Industrial Engineering 55(1): 64–79. https://doi.org/10.1016/j.cie.2007.12.001
Stević, Ž.; Pamučar, D.; Zavadskas, E. K.; Ćirović, G.; Prentkovskis, O. 2017. The selection of wagons for the internal transport of a logistics company: a novel approach based on rough BWM and rough SAW methods, Symmetry 9(11): 264. https://doi.org/10.3390/sym9110264
Vlachopoulou, M.; Silleos, G.; Manthou, V. 2001. Geographic information systems in warehouse site selection decisions, International Journal of Production Economics 71(1–3): 205–212. https://doi.org/10.1016/S0925-5273(00)00119-5
Wei, G.; Wang, X. 2007. Some geometric aggregation operators based on interval-valued intuitionistic fuzzy sets and their application to group decision making, in 2007 International Conference on Computational Intelligence and Security (CIS 2007), 15–19 December 2007, Harbin, China, 495–499. https://doi.org/10.1109/CIS.2007.84
Xu, Z. 2007a. Methods for aggregating interval-valued intuitionistic fuzzy information and their application to decision making, Control and Decision 22(2): 215–219.
Xu, Z. 2007b. Multi-person multi-attribute decision making models under intuitionistic fuzzy environment, Fuzzy Opti mization and Decision Making 6(3): 221–236. https://doi.org/10.1007/s10700-007-9009-7
Xu, Z.-S.; Chen, J. 2007. Approach to group decision making based on interval-valued intuitionistic judgment matrices, Systems Engineering – Theory & Practice 27(4): 126–133. https://doi.org/10.1016/S1874-8651(08)60026-5
Xue, Y.-X.; You, J.-X.; Lai, X.-D.; Liu, H.-C. 2016. An interval-valued intuitionistic fuzzy MABAC approach for material selection with incomplete weight information, Applied Soft Computing 38: 703–713. https://doi.org/10.1016/j.asoc.2015.10.010
Yan, H.; Tang, S.-L. 2009. Pre-distribution and post-distribution cross-docking operations, Transportation Research Part E: Logistics and Transportation Review 45(6): 843–859. https://doi.org/10.1016/j.tre.2009.05.005
Yu, D.; Wu, Y.; Lu, T. 2012. Interval-valued intuitionistic fuzzy prioritized operators and their application in group decision making, Knowledge-Based Systems 30: 57–66. https://doi.org/10.1016/j.knosys.2011.11.004
Zadeh, L. A. 1965. Fuzzy sets, Information and Control 8(3): 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zavadskas, E. K.; Antuchevičienė, J.; Razavi Hajiagha, S. H.; Hashemi, S. S. 2014. Extension of weighted aggregated sum product assessment with interval-valued intuitionistic fuzzy numbers (WASPAS-IVIF), Applied Soft Computing 24: 1013–1021. https://doi.org/10.1016/j.asoc.2014.08.031
Zavadskas, E. K.; Antuchevičienė, J.; Razavi Hajiagha, S. H.; Hashemi, S. S. 2015. The interval-valued intuitionistic fuzzy MULTIMOORA method for group decision making in engineering, Mathematical Problems in Engineering 2015: 560690. https://doi.org/10.1155/2015/560690
Zavadskas, E. K.; Nunić, Z.; Stjepanović, Ž.; Prentkovskis, O. 2018a. A novel rough range of value method (R-ROV) for selecting automatically guided vehicles (AGVs), Studies in Informatics and Control 27(4): 385–394. https://doi.org/10.24846/v27i4y201802
Zavadskas, E. K.; Stević, Ž.; Tanackov, I.; Prentkovskis, O. 2018b. A novel multicriteria approach – rough step-wise weight assessment ratio analysis method (R-SWARA) and its application in logistics, Studies in Informatics and Control 27(1): 97–106. https://doi.org/10.24846/v27i1y201810