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Green Supplier Selection Using Improved TOPSIS and Best-Worst Method Under Intuitionistic Fuzzy Environment
Volume 29, Issue 4 (2018), pp. 773–800
Pub. online: 1 January 2018      Type: Research Article      Open accessOpen Access
Received
1 June 2017
Accepted
1 July 2018
Published
1 January 2018

Abstract

Green supplier selection has recently become one of the key strategic considerations in green supply chain management, due to regulatory requirements and market trends. It can be regarded as a multi-criteria group decision-making (MCGDM) problem, in which a set of alternatives are evaluated with respect to multiple criteria. MCGDM methods based on Analytic Hierarchy Process (AHP) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) are widely used in solving green supplier selection problems. However, the classic AHP must conduct large amounts of pairwise comparisons to derive a consistent result due to its complex structure. Meanwhile, the classic TOPSIS only considers one single negative idea solution in selecting suppliers, which is insufficiently cautious. In this study, an improved TOPSIS integrated with Best-Worst Method (BWM) is developed to solve MCGDM problems with intuitionistic fuzzy information in the context of green supplier selection. The BWM is investigated to derive criterion weights, and the improved TOPSIS method is proposed to obtain decision makers’ weights in terms of different criteria. Moreover, the developed TOPSIS-based coefficient is used to rank alternatives. Finally, a green supplier selection problem in the agri-food industry is presented to validate the proposed approach followed by sensitivity and comparative analyses.

References

 
Akman, G. (2015). Evaluating suppliers to include green supplier development programs via Fuzzy c-means and VIKOR methods. Computers & Industrial Engineering, 86, 69–82.
 
Aloini, D., Dulmin, R., Mininno, V. (2014). A peer IF-TOPSIS based decision support system for packaging machine selection. Expert Systems with Applications, 41(5), 2157–2165.
 
Aouadni, S., Rebai, A., Turskis, Z. (2017). The meaningful mixed data TOPSIS (TOPSIS-MMD) method and its application in supplier selection. Studies in Informatics & Control, 26(3), 353–363.
 
Atanassov, K.T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.
 
Atanassov, K.T. (1994). New operations defined over the intuitionistic fuzzy sets. Fuzzy Sets and Systems, 61(2), 137–142.
 
Awasthi, A., Kannan, G. (2016). Green supplier development program selection using NGT and VIKOR under fuzzy environment. Computers & Industrial Engineering, 91, 100–108.
 
Banaeian, N., Nielsen, I.E., Mobli, H., Omid, M. (2014). Green supplier selection in edible oil production by a hybrid model using Delphi method and green data envelopment analysis (GDEA). Management and Production Engineering Review, 5(4), 3–8.
 
Banaeian, N., Mobli, H., Nielsen, I.E., Omid, M. (2015). Criteria definition and approaches in green supplier selection—a case study for raw material and packaging of food industry. Production & Manufacturing Research, 3(1), 149–168.
 
Banaeian, N., Mobli, H., Fahimnia, B., Nielsen, I.E., Omid, M. (2018). Green supplier selection using fuzzy group decision making methods: a case study from the agri-food industry. Computers & Operations Research, 89, 337–347.
 
Beske, P., Land, A., Seuring, S. (2014). Sustainable supply chain management practices and dynamic capabilities in the food industry: a critical analysis of the literature. International Journal of Production Economics, 152, 131–143.
 
Blome, C., Hollos, D., Paulraj, A. (2014). Green procurement and green supplier development: antecedents and effects on supplier performance. International Journal of Production Research, 52(1), 32–49.
 
Borghi, A.D., Gallo, M., Strazza, C., Borghi, M.D. (2014). An evaluation of environmental sustainability in the food industry through life cycle assessment: the case study of tomato products supply chain. Journal of Cleaner Production, 78, 121–130.
 
Brodt, S., Kramer, K.J., Kendall, A., Feenstra, G. (2013). Comparing environmental impacts of regional and national-scale food supply chains: a case study of processed tomatoes. Food Policy, 42, 106–114.
 
Buyukozkan, G., Cifci, G. (2012). A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers. Expert Systems with Applications, 39(3), 3000–3011.
 
Buyukozkan, G., Guleryuz, S. (2016). A new integrated intuitionistic fuzzy group decision making approach for product development partner selection. Computers & Industrial Engineering, 102, 383–395.
 
Cabrerizo, F.J., Herrera-Viedma, E., Pedrycz, W. (2013). A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts. European Journal of Operational Research, 230(3), 624–633.
 
Cabrerizo, F.J., Chiclana, F., Al-Hmouz, R., Morfeq, A., Balamash, A.S., Herrera-Viedma, E. (2015). Fuzzy decision making and consensus: challenges. Journal of Intelligent & Fuzzy Systems, 29(3), 1109–1118.
 
Cabrerizo, F.J., Al-Hmouz, R., Morfeq, A., Balamash, A.S., Martínez, M.A., Herrera-Viedma, E. (2017). Soft consensus measures in group decision making using unbalanced fuzzy linguistic information. Soft Computing, 21(11), 3037–3050.
 
Chai, J., Liu, J.N.K., Ngai, E.W.T. (2013). Application of decision-making techniques in supplier selection: a systematic review of literature. Expert Systems with Applications, 40(10), 3872–3885.
 
Chen, S., Cheng, S., Lan, T. (2016). Multicriteria decision making based on the TOPSIS method and similarity measures between intuitionistic fuzzy values. Information Sciences, 367–368, 279–295.
 
Chu, M., Shyu, J., Tzeng, G., Khosla, R. (2007). Comparison among three analytical methods for knowledge communities group-decision analysis. Expert Systems with Applications, 33(4), 1011–1024.
 
Dobos, I., Vorosmarty, G. (2014). Green supplier selection and evaluation using DEA-type composite indicators. International Journal of Production Economics, 157(1), 273–278.
 
Dong, Y., Zhang, H., Herrera-Viedma, E. (2016). Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors. Decision Support Systems, 84, 1–15.
 
Dou, Y., Zhu, Q., Sarkis, J. (2014). Evaluating green supplier development programs with a grey-analytical network process-based methodology. European Journal of Operational Research, 233(2), 420–431.
 
Dwivedi, G., Srivastava, R.K., Srivastava, S.K. (2018). A generalised fuzzy TOPSIS with improved closeness coefficient. Expert Systems with Applications, 96, 185–195.
 
Fahimnia, B., Sarkis, J., Davarzani, H. (2015a). Green supply chain management: a review and bibliometric analysis. International Journal of Production Economics, 162, 101–114.
 
Fahimnia, B., Sarkis, J., Eshragh, A. (2015b). A tradeoff model for green supply chain planning: a leanness-versus-greenness analysis. Omega, 54, 173–190.
 
Freeman, J., Chen, T. (2015). Green supplier selection using an AHP-Entropy-TOPSIS framework. Supply Chain Management, 20(3), 327–340.
 
Ghorabaee, M.K., Zavadskas, E.K., Amiri, M., Esmaeili, A. (2016). Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets. Journal of Cleaner Production, 137, 213–229.
 
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(1), 66–83.
 
Hashemi, S.H., Karimi, A., Tavana, M. (2015). An integrated green supplier selection approach with analytic network process and improved Grey relational analysis. International Journal of Production Economics, 159, 178–191.
 
Hwang, C.L., Yoon, K.P. (1981). Multiple Attribute Decision Making: Methods and Application. Springer, New York.
 
Kahraman, C., Oztaysi, B., Onar, S.C. (2016). A comprehensive literature review of 50 years of fuzzy set theory. International Journal of Computational Intelligence Systems, 9(1), 3–24.
 
Kannan, D., Jabbour, A.B.L.D., Jabbour, C.J.C. (2014). Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company. European Journal of Operational Research, 233(2), 432–447.
 
Kannan, D., Govindan, K., Rajendran, S. (2015). Fuzzy axiomatic design approach based green supplier selection: a case study from Singapore. Journal of Cleaner Production, 96, 194–208.
 
Kuo, T.C., Hsu, C., Li, J. (2015). Developing a green supplier selection model by using the DANP with VIKOR. Sustainability, 7(2), 1661–1689.
 
Li, J., Wang, J. (2017). An extended QUALIFLEX method under probability hesitant fuzzy environment for selecting green suppliers. International Journal of Fuzzy Systems, 19(6), 1866–1879.
 
Li, W.W., Wu, C. (2016). A multicriteria interval-valued intuitionistic fuzzy set TOPSIS decision-making approach based on the improved score function. Journal of Intelligent Systems, 25(2), 239–250.
 
Li, J., Wang, J., Hu, J. (2018). Multi-criteria decision-making method based on dominance degree and BWM with probabilistic hesitant fuzzy information. International Journal of Machine Learning and Cybernetics. https://doi.org/10.1007/s13042-018-0845-2.
 
Liao, C., Fu, Y., Wu, L. (2016). Integrated FAHP, ARAS-F and MSGP methods for green supplier evaluation and selection. Technological and Economic Development of Economy, 22(5), 651–669.
 
Mou, Q., Xu, Z., Liao, H. (2016). An intuitionistic fuzzy multiplicative best-worst method for multi-criteria group decision making. Information Sciences, 374, 224–239.
 
Mufazzal, S., Muzakkir, S.M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, 119, 427–438.
 
Nie, R.X., Wang, J.Q., Li, L. (2017). A shareholder voting method for proxy advisory firm selection based on 2-tuple linguistic picture preference relation. Applied Soft Computing, 60, 520–539.
 
Nie, R.X., Tian, Z.P., Wang, J.Q., Zhang, H.Y., Wang, T.L. (2018a). Water security sustainability evaluation: Applying a multistage decision support framework in industrial region. Journal of Cleaner Production, 196, 1681–1704.
 
Nie, R.X., Tian, Z.P., Wang, X.K., Wang, J.Q., Wang, T.L. (2018b). Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2018.05.030.
 
Opricovic, S., Tzeng, G.H. (2004). Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445–455.
 
Qin, J., Liu, X., Pedrycz, W. (2017). An extended TODIM multi-criteria group decision making method for green supplier selection in interval type-2 fuzzy environment. European Journal of Operational Research, 258(2), 626–638.
 
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.
 
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: some properties and a linear model. Omega, 64, 126–130.
 
Rezaei, J., Wang, J., Tavasszy, L. (2015). Linking supplier development to supplier segmentation using Best Worst Method. Expert Systems with Applications, 42(23), 9152–9164.
 
Roostaee, R., Izadikhah, M., Lotfi, F.H., Rostamy-Malkhalifeh, M. (2012). A multi-criteria intuitionistic fuzzy group decision making method for supplier selection with VIKOR method. International Journal of Fuzzy System Applications, 2(1), 1–17.
 
Saaty, T.L. (1980). The Analytic Hierarchy Process. McGraw-Hill, Columbus.
 
Saaty, T.L. (1996). Decision Making with Dependence and Feedback: The Analytic Network Process. RWS, Pittsburgh.
 
Shen, K., Wang, J. (2018). Z-VIKOR method based on a new weighted comprehensive distance measure of Z-number and its application. IEEE Transactions on Fuzzy Systems. https://doi.org/10.1109/TFUZZ.2018.2816581.
 
Shen, F., Ma, X., Li, Z., Xu, Z., Cai, D. (2018). An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation. Information Sciences, 428, 105–119.
 
Szmidt, E., Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems, 114(3), 505–518.
 
Tian, Z.P., Wang, J.Q., Wang, J., Zhang, H.Y. (2018a). A multi-phase QFD-based hybrid fuzzy MCDM approach for performance evaluation: a case of smart bike-sharing programs in Changsha. Journal of Cleaner Production, 171, 1068–1083.
 
Tian, Z.P., Wang, J.Q., Zhang, H.Y. (2018b). An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2018.03.037.
 
Tsui, C., Tzeng, G., Wen, U. (2015). A hybrid MCDM approach for improving the performance of green suppliers in the TFT-LCD industry. International Journal of Production Research, 53(21), 1–19.
 
Vahidi, F., Torabi, S.A., Ramezankhani, M.J. (2018). Sustainable supplier selection and order allocation under operational and disruption risks. Journal of Cleaner Production, 174, 1351–1365.
 
Vazquez-Brust, D.A., Sarkis, J. (2012). Green Growth: Managing the Transition to Sustainable Economies. Springer, Netherlands.
 
Wan, S., Xu, G., Wang, F., Dong, J. (2015). A new method for Atanassov’s interval-valued intuitionistic fuzzy MAGDM with incomplete attribute weight information. Information Sciences, 316, 329–347.
 
Wang, X., Chan, H.K. (2013). A hierarchical fuzzy TOPSIS approach to assess improvement areas when implementing green supply chain initiatives. International Journal of Production Research, 51(10), 3117–3130.
 
Wang, J., Wang, J.Q., Zhang, H.Y., Chen, X.H. (2017). Distance-based multi-criteria group decision-making approaches with multi-hesitant fuzzy linguistic information. International Journal of Information Technology & Decision Making, 16(4), 1069–1099.
 
Wang, L., Zhang, H., Wang, J., Li, L. (2018). Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project. Applied Soft Computing, 64, 216–226.
 
Xu, Z. (2005). An overview of methods for determining OWA weights. International Journal of Intelligent Systems, 20(8), 843–865.
 
Xu, Z. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems, 15(6), 1179–1187.
 
Yu, S.M., Wang, J., Wang, J.Q. (2018). An extended TODIM approach with intuitionistic linguistic numbers. International Transactions in Operational Research, 25(3), 781–805.
 
Yue, Z.L. (2014). TOPSIS-based group decision-making methodology in intuitionistic fuzzy setting. Information Sciences, 277, 141–153.
 
Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
 
Zavadskas, E.K., Mardani, A., Turskis, Z., Jusoh, A., Nor, K.M. (2016). Development of TOPSIS method to solve complicated decision-making problems: An overview on developments from 2000 to 2015. International Journal of Information Technology & Decision Making, 15(3), 645–682.
 
Zyoud, S.H., Fuchs-Hanusch, D. (2017). A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, 78, 158–181.

Biographies

Tian Zhang-Peng

Z.P. Tian received his MS degree in management science and engineering form Central South University, Changsha, China, in 2016. He is currently working towards the PhD degree at the Business School, Central South University. His current research interests include decision-making theory and application, risk management and control, and information management.

Zhang Hong-Yu

H.Y. Zhang received the MS degree in computer software and theory and the PhD degree in management science and engineering from Central South University, Changsha, China, in 2005 and 2009, respectively. She is currently an associate professor at the Business School, Central South University. Her research interests include information management and its applications in production operations. Her current research focuses on remanufacturing production management and decision-making theory.

Wang Jian-Qiang
jqwang@csu.edu.cn

J.Q. Wang received the PhD degree in management science and engineering from Central South University, Changsha, China, in 2005. He is currently a professor at the Business School, Central South University. His current research interests include decision-making theory and application, risk management and control, and information management.

Wang Tie-Li

T.L. Wang received the PhD degree in management science and engineering from Central South University, Changsha, China, in 2007. She is currently a professor at Management School, University of South China. His current research interests include decision-making theory and application, risk management and control, and nuclear emergency decision-making.


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Keywords
supplier selection group decision-making best-worst method intuitionistic fuzzy sets

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