Sustainable supplier selection by a new possibilistic hierarchical model in the context of Z-information

In recent years, supplier selection has been significantly important with respect to dimensions and criteria of sustainability. Organizations need to try to choose their suppliers based on how well their performances are in each of the economic, social, and environmental criteria. On the other hand, since the methods of supplier selection depend on the experts’ opinions, which have the potential of uncertainty and ambiguity, using Fuzzy sets to evaluate the criteria can be useful. Apart from considering experts’ opinions on a fuzzy basis, probabilities are considered in experts’ opinions via Z-numbers in order to increase the reliability of the data and the results. In this paper, after reviewing the literature, identifying the sustainability criteria, and step-by-step explaining the presented method, a numerical example is studied for more clarification. Moreover, the results of the conventional fuzzy sets are obtained and have been compared with those considering the probabilities (Z-number) leading to the conclusion that applying the experts’ opinions will be effective in ranking the suppliers.

[1]  Narges Banaeian,et al.  Green supplier selection using fuzzy group decision making methods: A case study from the agri-food industry , 2018, Comput. Oper. Res..

[2]  R. Yager.  Ranking fuzzy subsets over the unit interval , 1978, 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes.

[3]  Jian-qiang Wang,et al.  Z-VIKOR Method Based on a New Comprehensive Weighted Distance Measure of Z-Number and Its Application , 2018, IEEE Transactions on Fuzzy Systems.

[4]  Kuan Yew Wong,et al.  A decision support model for sustainable supplier selection in sustainable supply chain management , 2017, Comput. Ind. Eng..

[5]  Lotfi A. Zadeh,et al.  A Note on Z-numbers , 2011, Inf. Sci..

[6]  Reza Tavakkoli-Moghaddam,et al.  A hybrid Z-number data envelopment analysis and neural network for assessment of supply chain resilience: a case study , 2018, Central Eur. J. Oper. Res..

[7]  Ardeshir Bahreininejad,et al.  Sustainable supplier selection: A ranking model based on fuzzy inference system , 2012, Appl. Soft Comput..

[8]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[9]  Fuyuan Xiao,et al.  A Hybrid Fuzzy Soft Sets Decision Making Method in Medical Diagnosis , 2018, IEEE Access.

[10]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[11]  Syed Waqar Haider,et al.  Identifying and bridging the attitude-behavior gap in sustainable transportation adoption , 2019, J. Ambient Intell. Humaniz. Comput..

[12]  Christer Carlsson,et al.  Possibilistic mean value and variance of fuzzy numbers: Some examples of application , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[13]  Ersin Namli,et al.  A living environment prediction model using ensemble machine learning techniques based on quality of life index , 2019, Journal of Ambient Intelligence and Humanized Computing.

[14]  Morteza Bagherpour,et al.  Developing an integrated index to assess social sustainability in construction industry using fuzzy logic , 2019, Journal of Cleaner Production.

[15]  Sepehr Hendiani,et al.  Development of sustainability index using Z-numbers: a new possibilistic hierarchical model in the context of Z-information , 2019, Environment, Development and Sustainability.

[16]  E. Ertugrul Karsak,et al.  A QFD-based fuzzy MCDM approach for supplier selection , 2013 .

[17]  C. Kahraman,et al.  Multi‐criteria supplier selection using fuzzy AHP , 2003 .

[18]  Hacer Güner Gören.  A decision framework for sustainable supplier selection and order allocation with lost sales , 2018 .

[19]  Yong Deng,et al.  A Method of Converting Z-number to Classical Fuzzy Number , 2012 .

[20]  Amir Abbas Najafi,et al.  Portfolio Selection Using Z-Number Theory: Two Solution Methodologies , 2018, Int. J. Fuzzy Syst..

[21]  Amir Karimi,et al.  Analysis of interactions among sustainability supplier selection criteria using ISM and fuzzy DEMATEL , 2014, Int. J. Appl. Decis. Sci..

[22]  Ziqiang Zeng,et al.  A Fuzzy Multi-criteria Decision Making Approach for Supplier Selection Under Fuzzy Environment , 2017 .

[23]  Bingyi Kang,et al.  Environmental assessment under uncertainty using Dempster–Shafer theory and Z-numbers , 2019, Journal of Ambient Intelligence and Humanized Computing.

[24]  Xiongyong Zhou,et al.  An Integrated Sustainable Supplier Selection Approach Based on Hybrid Information Aggregation , 2018, Sustainability.

[25]  Shyi-Ming Chen.  FUZZY SYSTEM RELIABILITY-ANALYSIS USING FUZZY NUMBER ARITHMETIC OPERATIONS (VOL 64, PG 31, 1994) , 1994 .

[26]  Shuo-Yan Chou,et al.  A Fuzzy MCDM Approach for Green Supplier Selection from the Economic and Environmental Aspects , 2016 .

[27]  Liguo Fei,et al.  A new vector valued similarity measure for intuitionistic fuzzy sets based on OWA operators , 2019 .

[28]  Yu Yang,et al.  SMAA-based model for decision aiding using regret theory in discrete Z-number context , 2018, Appl. Soft Comput..

[29]  Jian-Bo Yang,et al.  On the centroids of fuzzy numbers , 2006, Fuzzy Sets Syst..

[30]  Kevin Cullinane,et al.  A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy , 2019, Expert Syst. Appl..

[31]  Hui Yu,et al.  Emergency Alternative Selection Based on an E-IFWA Approach , 2019, IEEE Access.

[32]  He-Yau Kang,et al.  A green supplier selection model for high-tech industry , 2009, Expert Syst. Appl..

[33]  Felix T.S. Chan,et al.  Integrating environmental criteria into the supplier selection process , 2003 .

[34]  Ching-Torng Lin,et al.  Agility evaluation using fuzzy logic , 2006 .

[35]  Shahram Rahimi,et al.  Integration of Z-numbers and Bayesian decision theory: A hybrid approach to decision making under uncertainty and imprecision , 2018, Appl. Soft Comput..

[36]  Chen-Tung Chen,et al.  A fuzzy approach for supplier evaluation and selection in supply chain management , 2006 .

[37]  Xiaowei Xu,et al.  Multi-criteria decision making approaches for supplier evaluation and selection: A literature review , 2010, Eur. J. Oper. Res..

[38]  J. Adamo.  Fuzzy decision trees , 1980 .

[39]  Kai Zhu,et al.  A three-echelon supply chain with asymmetric information under uncertainty , 2019, J. Ambient Intell. Humaniz. Comput..

[40]  Masoud Rahiminezhad Galankashi,et al.  ScienceDirect 12 th Global Conference on Sustainable Manufacturing Prioritizing Green Supplier Selection Criteria using Fuzzy Analytical Network Process , 2015 .

[41]  Mohammad Jafar Tarokh,et al.  A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting , 2011, Expert Syst. Appl..

[42]  K. Mahler,et al.  An unsolved problem on the powers of 3/2 , 1968, Journal of the Australian Mathematical Society.

[43]  Gülçin Büyüközkan,et al.  A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information , 2011, Comput. Ind..

[44]  K. Govindan,et al.  Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach , 2018 .

[45]  Yong Deng,et al.  An interval-valued Pythagorean prioritized operator-based game theoretical framework with its applications in multicriteria group decision making , 2019, Neural Computing and Applications.

[46]  Burcu Avcı Öztürk,et al.  Sustainable Supplier Selection with A Fuzzy Multi-Criteria Decision Making Method Based on Triple Bottom Line , 2014 .

[47]  Jianqiang Wang,et al.  Multi-criteria PROMETHEE method based on possibility degree with Z-numbers under uncertain linguistic environment , 2020, J. Ambient Intell. Humaniz. Comput..

[48]  Rajesh Joshi,et al.  A new multi-criteria decision-making method based on intuitionistic fuzzy information and its application to fault detection in a machine , 2019, Journal of Ambient Intelligence and Humanized Computing.

[49]  Chih-Hung Wu,et al.  Fuzzy DEMATEL method for developing supplier selection criteria , 2011, Expert Syst. Appl..

[50]  Barrie Dale,et al.  The buyer-supplier relationship in total quality management , 1989 .

[51]  Elizabeth Chang,et al.  ZBWM: The Z-number extension of Best Worst Method and its application for supplier development , 2018, Expert Syst. Appl..