A comparative study of integrated FMCDM methods for evaluation of organizational strategy development
With the globalization of economy and development of technology, organizational strategy development in distribution channel management has become more significant for competitive business world. To improve distribution channel performance, many companies have focused on Multi-Criteria Decision Making (MCDM) methods. In the literature, there are a great number of studies on MCDM and fuzzy MCDM (FMCDM) methods, whereas a few studies on integrated FMCDM methods. The purpose of this study is to propose integrated FMCDM methodology including FAHP, WASPAS-F, EDAS-F and ARAS-F. In these methods, relative importances of the criteria are determined by FAHP. Managerial and financial perspective is determined as the most important criteria by FAHP methods. Then WASPAS-F, EDAS-F and ARAS-F methods are carried out to rank the alternatives. The practical implication of the integrated FMCDM methods is the use of linguistic variables for assessment of the criteria and the alternatives. As a research implication, Hybrid Based Strategy is determined as the best organizational strategy. The originality and value of study is to present comparative analyzes using the newly developed WASPAS-F, EDAS-F and ARAS-F integrated with FAHP methods. An important finding of the study is that the ranking results of the proposed methods are consistent with each other.
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Ateş, N. Y., Çevik, S., Kahraman, C., Gülbay, M., & Erdoğan, S. A. (2006). Multi attribute performance evaluation using a hierarchical fuzzy TOPSIS method. In C. Kahraman (Ed.), Fuzzy applications in industrial engineering. Studies in fuzziness and computing (pp. 537-572). Berlin, Heidelberg: Springer. https://doi.org/10.1007/3-540-33517-x_22
Baležentis, A., Baležentis, T., & Misiunas, A. (2012). An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods. Technological and Economic Development of Economy, 18(1), 34-53. https://doi.org/10.3846/20294913.2012.656151
Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), 141-164. https://doi.org/10.1287/mnsc.17.4.b141
Brauers, W., & Zavadskas, E. (2006). The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35, 445-469.
Brauers, W. K. M., Baležentis, A., & Baležentis, T. (2011). MultiMOORA for the EU member states updated with fuzzy number theory. Technological and Economic Development of Economy, 17, 259-290. https://doi.org/10.3846/20294913.2011.580566
Brauers, W. K. M., & Zavadskas, E. K. (2010). Project management by MultiMOORA as an instrument for transition economies. Ukio Technologinis ir Ekonominis Vystymas, 16, 5-24. https://doi.org/10.3846/tede.2010.01
Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247. https://doi.org/10.1016/0165-0114(85)90090-9
Büyüközkan, G., Arsenyan, J., & Ruan, D. (2012). Logistics tool selection with two–phase fuzzy multi criteria decision making: a case study for personal digital assistant selection. Expert Systems with Applications, 39(1), 142-153. https://doi.org/10.1016/j.eswa.2011.06.017
Cavallaro, F. (2015). A Takagi-Sugeno fuzzy inference system for developing a sustainability index of biomass. Sustainability, 7(9), 12359-12371. https://doi.org/10.3390/su70912359
Çelik, E., Gül, M., Aydın, N., Gümüş, A. T., & Güneri, A. F. (2015). A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets. Knowledge-based Systems, 85, 329-341. https://doi.org/10.1016/j.knosys.2015.06.004
Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649-655. https://doi.org/10.1016/0377-2217(95)00300-2
Chauhan, A., & Singh, A. (2016). A hybrid multi-criteria decision making method approach for selecting a sustainable location of healthcare waste disposal facility. Journal of Cleaner Production, 139, 1001-1010. https://doi.org/10.1016/j.jclepro.2016.08.098
Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets and Systems, 114, 1-9. https://doi.org/10.1016/S0165-0114(97)00377-1
Coughlan, A. T., Anderson, E., Stern, L. W., & El-Ansary, A. I. (2006). Marketing channels (7th ed.). New Jersey: Pearson/Prentice Hall.
Dinçer, H., Hacıoğlu, U., Tatoğlu, E., & Delen, D. (2016). A fuzzy-hybrid analytic model to assess investors᾽ perceptions for industry selection. Decision Support Systems, 86, 24-34. https://doi.org/10.1016/j.dss.2016.03.005
Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: a literature review. Journal of Cleaner Production, 98, 66-83. https://doi.org/10.1016/j.jclepro.2013.06.046
Guan, W. (2010). Developments in distribution channels − a case study of a timber product distribution channel (PhD thesis). Linköping Studies in Science and Technology, Linköping University, Sweeden.
Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of Evaluation based on Distance from Average Solution (EDAS). Informatica, 26(3), 435-451. https://doi.org/10.15388/informatica.2015.57
Ghorabaee, M., Zavadskas, E. K., Amiri, M., & Turskis, Z. (2016). Extended EDAS method for fuzzy multi-criteria decision-making: an application to supplier selection. International Journal of Computers, Communications & Control (IJCCC), 11(3), 358-371. https://doi.org/10.15837/ijccc.2016.3.2557
Ghorabaee, M., Amiri, M., Olfat, L., & Khatami Firouzabadi, S. M. A. (2017). Designing a multi-product multi-period supply chain network with reverse logistics and multiple objectives under uncertainty. Technological and Economic Development of Economy, 23(3), 520-548. https://doi.org/10.1016/j.psep.2016.02.013
Hajkowicz, S., & Collins, K. (2007). A review of multiple criteria analysis for water resource planning and management. Water Resources Management, 21(9), 1553-1566. https://doi.org/10.1007/s11269-006-9112-5
Ho, W., Xu, X., & Dey, P. K. (2010). Multi-criteria decision making approaches for supplier evaluation and selection: a literature review. European Journal of Operational Research, 202(1), 16-24. https://doi.org/10.1016/j.ejor.2009.05.009
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making – methods and applications. New York: Springer. https://doi.org/10.1007/978-3-642-48318-9
Jato-Espino, D., Castillo-Lopez, E., Rodriguez-Hernandez, J., & Canteras-Jordana, J. C. (2014). A review of application of multi-criteria decision making methods in construction. Automation in Construction, 45, 151-162. https://doi.org/10.1016/j.autcon.2014.05.013
Kahraman, C., Engin, O., Kabak, O., & Kaya, İ. (2009). Information systems outsourcing decisions using a group decision-making approach. Engineering Applications of Artificial Intelligence, 22(6), 832-841. https://doi.org/10.1016/j.engappai.2008.10.009
Kahraman, C., Onar, S. Ç., & Öztayşi, B. (2015). Fuzzy multicriteria decision-making: a literature review. International Journal of Computational Intelligence Systems, 8(4), 637-666. https://doi.org/10.1080/18756891.2015.1046325
Kahraman, C., Ghorabaee, M., Zavadskas, E. K., Çevik Onar, S., Yazdani, M., & Öztayşi, 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
Kannan, D., Khodaverd, I. R., Olfat, L., Jafarian, A., & Diabat, A. (2013). Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain. Journal of Cleaner Production, 47, 355-367. https://doi.org/10.1016/j.jclepro.2013.02.010
Kaya, T., & Kahraman, C. (2010). Multicriteria renewable energy planning using an integrated fuzzy VIKOR& AHP methodology: the case of Istanbul. Energy, 35(6), 2517-2527. https://doi.org/10.1016/j.energy.2010.02.051
Kaya, T., & Kahraman, C. (2011). Fuzzy multiple criteria forestry decision making based on an integrated VIKOR and AHP approach. Expert Systems with Applications, 38(6), 7326-7333. https://doi.org/10.1016/j.eswa.2010.12.003
Liao, C. N., Fu, Y. K., & Wu, L. C. (2016). Integrated FAHP, ARAS-F and MSGP methods for green supplier evaluation and selection. Technological and Economic Development of Economy, 22(5), 651-669. https://doi:10.3846/20294913.2015.1072750
Mardani, A., Jusoh, A., Md Nor, K., Khalifah, Z., Zakwan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014. Economic Research-Ekonomska Istraživanja, 28(1), 516-571. https://doi.org/10.1080/1331677x.2015.1075139
Mardani, A., Nilashi, M., Zakuan, N., Loganathan, N., Soheilirad, S., Saman, M. Z. M., & Ibrahim, O. (2017). A systematic review and meta-analysis of SWARA and WASPAS methods: theory and applications with recent fuzzy developments. Applied Soft Computing, 57, 265-292. https://doi.org/10.1016/j.asoc.2017.03.045
Mikhailov, L. (2002). Fuzzy analytical approach to partnership selection in formation of virtual enterprises. Omega, 30, 393-401. https://doi.org/10.1016/S0305-0483(02)00052-X
Mikhailov, L. (2003). Deriving priorities from fuzzy pairwise comparison judgements. Fuzzy Sets and Systems, 134(3), 365-385. https://doi.org/10.1016/s0165-0114(02)00383-4
Mikhailov, L., & Tsvetinov, P. (2004). Evaluation of services using a fuzzy analytic hierarchy process. Applied Soft Computing, 5(1), 23-33. https://doi.org/10.1016/j.asoc.2004.04.001
Nguyen, H. T., Md Dawal, S. Z., Nukman, Y., Rifai, A. P., & Aoyama, H. (2016). An integrated MCDM model for conveyor equipment evaluation and selection in an FMC based on a fuzzy AHP and fuzzy ARAS in the presence of vagueness. PLoS ONE, 11(4), e0153222. https://doi.org/10.1371/journal.pone.0153222
Opricovic, S., & Tzeng, G. H. (2002). Multicriteria planning of post-earthquake sustainable reconstruction. Computer-Aided Civil and Infrastructure Engineering, 17, 211-220. https://doi.org/10.1111/1467-8667.00269
Opricovic, S. (2007). A fuzzy compromise solution for multicriteria problems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(3), 363-380. https://doi.org/10.1142/S0218488507004728
Önüt, S., Efendigil, T., & Kara, S. (2010). A combined fuzzy MCDM approach for selecting shopping center site: an example from Istanbul, Turkey. Expert Systems with Applications, 37(3), 1973-1980. https://doi.org/10.1016/j.eswa.2009.06.080
Paksoy, T., Pehlivan, N. Y., & Kahraman, C. (2012). Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS. Expert Systems with Applications, 39(3), 2822-2841. https://doi.org/10.1016/j.eswa.2011.08.142
Prakash, C., & Barua, M. K. (2015). Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment. Journal of Manufacturing Systems, 37, 599-615. https://doi.org/10.1016/j.jmsy.2015.03.001
Rangan, V. K., & Jaikumar, R. (1991). Integrating distribution strategy and tactics: a model and an application. Management Science, 37(11), 1377-1389. https://doi.org/10.1287/mnsc.37.11.1377
Rao, R. V. (2013). Decision making in the manufacturing environment: using graph theory and fuzzy multiple attribute decision making methods. London: Springer-Verlag. https://doi.org/10.1007/978-1-4471-4375-8
Rezaei, J. (2015). A systematic review of multi-criteria decision-making applications in reverse logistics. Transportation Research Procedia, 10, 766-776. https://doi.org/10.1016/j.trpro.2015.09.030
Rostamzadeh, R., Esmaeili, A., Nia, A. S., Saparauskas, J., & Ghorabaee, M. (2017). A fuzzy ARAS method for supply chain management performance measurement in smes under uncertainty. Transformations in Business & Economics, 16(2A), 319-348. https://doi.org/10.3846/16111699.2017.1279683
Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation. New York: McGraw-Hill.
Sabaei, D., Erkoyuncu, J., & Roy, R. (2015). A review of multi-criteria decision making methods for enhanced maintenance delivery. Procedia CIRP, 37, 30-35. https://doi.org/10.1016/j.procir.2015.08.086
Scott, J. A., Ho, W., & Dey, P. K. (2012). A review of multi-criteria decision-making methods for bio-energy systems. Energy, 42(1), 146-156. https://doi.org/10.1016/j.energy.2012.03.074
Senthil, S., Srirangacharyulu, B., & Ramesh, A. (2014). A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics. Expert Systems with Applications, 41(1), 50-58. https://doi.org/10.1016/j.eswa.2013.07.010
Soltani, A., Hewage, K., Reza, B., & Sadiq, R. (2015). Multiple stakeholders in multi-criteria decision-making in the context of Municipal Solid Waste Management: a review. Waste Management, 35, 318-328. https://doi.org/10.1016/j.wasman.2014.09.010
Sun, C. C. (2010). A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Systems with Applications, 37(12), 7745-7754. https://doi.org/10.1016/j.eswa.2010.04.066
Taylan, O., Bafail, A. O., Abdulaal, R. M. S., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116. https://doi.org/10.1016/j.asoc.2014.01.003
Turskis, Z., & Zavadskas, E. K. (2010). A new fuzzy additive ratio assessment method (ARAS‐F). Case study: the analysis of fuzzy multiple criteria in order to select the logistic centers location. Transport, 25(4), 423-432. https://doi.org/10.3846/transport.2010.52
Turskis, Z., Lazauskas, M., & Zavadskas, E. K. (2012). Fuzzy multiple criteria assessment of construction site alternatives for non-hazardous waste incineration plant in Vilnius city, applying ARAS-F and AHP methods. Journal of Environmental Engineering and Landscape Management, 20(2), 110-120. https://doi.org/10.3846/16486897.2011.645827
Turskis, Z., Zavadskas, E. K., Antucheviciene, J., & Kosareva, N. (2015). A hybrid model based on fuzzy AHP and fuzzy WASPAS for construction site selection. International Journal Of Computers Communications & Control, 10(6), 113-128. https://doi.org/10.15837/ijccc.2015.6.2078
Turskis, Z., Kersuliene, V., & Vinogradova, I. (2017). A new fuzzy hybrid multi-criteria decision-making approach to solve personnel assessment problems. Case study: directo selection for estates and economy office. Economic Computation and Economic Cybernetics Studies and Research, 51(3), 211-229. https://doi.org/10.3846/20294913.2011.635718
Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making methods and applications. Boca Raton, FL: Chapman and Hall/CRC.
Van Laarhoven, P. J. M., & Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1-3), 229-241. https://doi.org/10.1016/S0165-0114(83)80082-7
Zavadskas, E. K., Kaklauskas, A., & Sarka, V. (1994). The new method of multi-criteria complex proportional assessment of projects. Technological and Economic Development of Economy, 1(3), 131-139.
Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Technological and Economic Development of Economy, 16(2), 159-172. https://doi.org/10.3846/tede.2010.10
Zavadskas, E. K., Turskis, Z., Antucheviciene, J., & Zakarevicius, A. (2012). Optimization of weighted aggregated sum product assessment. Elektronika ir Elektrotechnika, 122(6), 1-6. http://dx.doi.org/10.5755/j01.eee.122.6.1810
Zimmermann, H. J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55. https://doi.org/10.1016/0165-0114(78)90031-3