Resilient Supplier Selection in a Supply Chain by a New Interval-Valued Fuzzy Group Decision Model Based on Possibilistic Statistical Concepts

Document Type: Research Paper

Authors

1 University of Tehran

2 School of Industrial Engineering College of Engineering, University of Tehran P.O. Box: 11155/4563, Tehran, IRAN

3 Shahed University

Abstract

Supplier selection is one the main concern in the context of supply chain networks by considering their global and competitive features. Resilient supplier selection as generally new idea has not been addressed properly in the literature under uncertain conditions. Therefore, in this paper, a new multi-criteria group decision-making (MCGDM) model is introduced with interval-valued fuzzy sets (IVFSs) and fuzzy possibilistic statistical concepts. Then, a new weighting method for the supply chain experts or decision makers (DMs) is presented under uncertainty in supply chain networks. Additionally, a modified version of an entropy method is extended for computing the weight of each assessment criterion. Possibilistic mean, standard deviation, and the cube-root of skewness are proposed within the MCGDM. In addition, a new fuzzy ranking method based on relative-closeness coefficients are proposed to rank the resilient supplier candidates. Finally, a resilient supplier selection problem is solved by the proposed group decision model to demonstrate its validity and is compared with a recent study.

Keywords

Main Subjects


Chen, X., Du, H., & Yang, Y. (2014). The interval-valued triangular fuzzy soft set and its method of dynamic decision making. Journal of Applied Mathematics, 2014.

Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1-14.

Cornelis, C., Deschrijver, G., & Kerre, E. E. (2006). Advances and challenges in interval-valued fuzzy logic. Fuzzy Sets and Systems, 157(5), 622-627.

Deng, X., & Li, R. (2014). Gradually tolerant constraint method for fuzzy portfolio based on possibility theory. Information Sciences, 259, 16-24.

Deng, X., Hu, Y., Deng, Y., & Mahadevan, S. (2014). Supplier selection using AHP methodology extended by D numbers. Expert Systems with Applications, 41(1), 156-167.

Deschrijver, G. (2007). Arithmetic operators in interval-valued fuzzy set theory. Information Sciences, 177(14), 2906-2924.

Dursun, M., & Karsak, E. E. (2013). A QFD-based fuzzy MCDM approach for supplier selection. Applied Mathematical Modelling, 37(8), 5864-5875.

Fazlollahtabar, H. (2016). An integration between fuzzy PROMETHEE and fuzzy linear program for supplier selection problem: Case study. Journal of Applied Mathematical Modelling and Computing, 1(1).

Guijun, W., & Xiaoping, L. (1998). The applications of interval-valued fuzzy numbers and interval-distribution numbers. Fuzzy Sets and Systems, 98(3), 331-335.

Haldar, A., Ray, A., Banerjee, D., & Ghosh, S. (2014). Resilient supplier selection under a fuzzy environment. International Journal of Management Science and Engineering Management, 9(2), 147-156.

Igoulalene, I., Benyoucef, L., & Tiwari, M. K. (2015). Novel fuzzy hybrid multi-criteria group decision making approaches for the strategic supplier selection problem. Expert Systems with Applications, 42(7), 3342-3356.

Jain, V., Sangaiah, A. K., Sakhuja, S., Thoduka, N., & Aggarwal, R. (2016). Supplier selection using fuzzy AHP and TOPSIS: A case study in the Indian automotive industry. Neural Computing and Applications, Article in Press, 1-10.

Junior, F. R. L., Osiro, L., & Carpinetti, L. C. R. (2014). A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection. Applied Soft Computing, 21, 194-209.

Jüttner, U., & Maklan, S. (2011). Supply chain resilience in the global financial crisis: an empirical study. Supply Chain Management: An International Journal, 16(4), 246-259.

Kamdem, J. S., Deffo, C. T., & Fono, L. A. (2012). Moments and semi-moments for fuzzy portfolio selection. Insurance: Mathematics and Economics, 51(3), 517-530.

Lee, S. M., & Rha, J. S. (2016). Ambidextrous supply chain as a dynamic capability: building a resilient supply chain. Management Decision, 54(1), 2-23.

Li, X., Qin, Z., & Kar, S. (2010). Mean-variance-skewness model for portfolio selection with fuzzy returns. European Journal of Operational Research, 202(1), 239-247.

Mari, S. I., Lee, Y. H., Memon, M. S., Park, Y. S., & Kim, M. (2015). Adaptivity of complex network topologies for designing resilient supply chain networks. International Journal of Industrial Engineering, 22(1), 102-116.

Matook, S., Lasch, R., & Tamaschke, R. (2009). Supplier development with benchmarking as part of a comprehensive supplier risk management framework. International Journal of Operations & Production Management, 29(3), 241-267.

Meindl, P., & Chopra, S. (2003). Supply Chain Management: Strategy, Planning, and Operation, 5 Ed.., Pearson Education India.

Memon, M. S., Lee, Y. H., & Mari, S. I. (2015). Group multi-criteria supplier selection using combined grey systems theory and uncertainty theory. Expert Systems with Applications, 42(21), 7951-7959.

Mensah, P., & Merkuryev, Y. (2014). Developing a resilient supply chain. Procedia-Social and Behavioral Sciences, 110, 309-319.

Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework. Journal of Business Logistics, 31(1), 1-21.

Ponis, S. T., & Koronis, E. (2012). Supply chain resilience: definition of concept and its formative elements. Journal of Applied Business Research, 28(5), 921.

Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20(1), 124-143.

Purvis, L., Spall, S., Naim, M., & Spiegler, V. (2016). Developing a resilient supply chain strategy during ‘boom’and ‘bust’. Production Planning & Control, 27(7-8), 579-590.

Rajesh, R., & Ravi, V. (2015). Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production, 86, 343-359.

Sahu, A. K., Datta, S., & Mahapatra, S. S. (2016). Evaluation and selection of resilient suppliers in fuzzy environment: Exploration of fuzzy-VIKOR. Benchmarking: An International Journal, 23(3), 651-673.

Sheffi, Y. (2005). Building a resilient supply chain. Harvard Business Review, 1(8), 1-4.

Wei, S. H., & Chen, S. M. (2009). Fuzzy risk analysis based on interval-valued fuzzy numbers. Expert Systems with Applications, 36(2), 2285-2299.

Yao, J.S., Lin, F.T. (2002). Constructing a fuzzy flow-shop sequencing model based on statistical data. International Journal of Approximate Reasoning, 29(3), 215–234.

Ye, F., & Lin, Q. (2013). Partner selection in a virtual enterprise: A group multiattribute decision model with weighted possibilistic mean values. Mathematical Problems in Engineering, 2013.

Yue, Z. (2011). An extended TOPSIS for determining weights of decision makers with interval numbers. Knowledge-Based Systems, 24(1), 146-153.

Zhang, W. G., Wang, Y. L., Chen, Z. P., & Nie, Z. K. (2007). Possibilistic mean–variance models and efficient frontiers for portfolio selection problem. Information Sciences, 177(13), 2787-2801.

Zheng, G., Yang, Y. E., & Zhou, L. (2014). Model and Genetic Algorithms for Resilient Supply Chain under Supply Disruption. In CICTP 2014: Safe, Smart, and Sustainable Multimodal Transportation Systems (pp. 821-829).