Evaluation and Ranking of Organizational Resilience Factors by Using a Two-Step Fuzzy AHP and Fuzzy TOPSIS

We presented a novel fuzzy multicriteria decision making approach to evaluate and rank organizational resilience factors with respect to user preference orders. Due to vagueness of the decision data, the precise numerical data are inadequate for real-life business situations. Human judgements can be expressed by linguistic expressions which are modeled by fuzzy sets. The complexity of the considered problem calls for analytic methods rather than intuitive decisions. Two fuzzy multi-criteria methods are proposed for solving the treated problem: Fuzzy Analytic Hierarchical Process (FAHP) is applied to determine the relative importance of business processes and the relative importance of organizational resilience factors under each business process, and an extension of the fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is applied to rank the organizational resilience factors. With respect to complexity and the type of considered management problem, we introduce a modified fuzzy decision matrix. The proposed algorithm has efficiently been applied in the assessment of organizational resilience factors to small and medium enterprises of the process industry.

[1]  Gülçin Büyüközkan,et al.  Using a multi-criteria decision making approach to evaluate mobile phone alternatives , 2007, Comput. Stand. Interfaces.

[2]  Scott Somers.  Measuring Resilience Potential: An Adaptive Strategy for Organizational Crisis Planning , 2009 .

[3]  Tien-Chin Wang,et al.  Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment , 2007, Expert Syst. Appl..

[4]  A. Valle,et al.  Diffusion of nuclear energy in some developing countries , 2014 .

[5]  Soheil Sadi-Nezhad,et al.  Preference ratio-based maximum operator approximation and its application in fuzzy flow shop scheduling , 2008, Appl. Soft Comput..

[6]  E. Stanley Lee,et al.  An extension of TOPSIS for group decision making , 2007, Math. Comput. Model..

[7]  C K Kwong,et al.  Determining the Importance Weights for the Customer Requirements in QFD Using a Fuzzy AHP with an Extent Analysis Approach , 2003 .

[8]  Shen Hong.  Intelligent Decision Support System Based on Agent , 2006 .

[9]  Huibert Kwakernaak,et al.  Rating and ranking of multiple-aspect alternatives using fuzzy sets , 1976, Autom..

[10]  Zhe Tian,et al.  Application of a trapezoidal fuzzy AHP method for work safety evaluation and early warning rating of hot and humid environments , 2012 .

[11]  Alev Taskin Gumus,et al.  Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology , 2009, Expert Syst. Appl..

[12]  George A. Fodor,et al.  Intelligent Decision Support System , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[13]  G. Bortolan,et al.  A review of some methods for ranking fuzzy subsets , 1985 .

[14]  F. Lootsma.  Fuzzy Logic for Planning and Decision Making , 1997 .

[15]  Yongtae Park,et al.  The changing pattern of SME's innovativeness through business model globalization , 2012 .

[16]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

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

[18]  Marcos R. S. Borges,et al.  Micro incident analysis framework to assess safety and resilience in the operation of safe critical systems: A case study in a nuclear power plant , 2008 .

[19]  Hans-Jürgen Zimmermann,et al.  Fuzzy Set Theory - and Its Applications , 1985 .

[20]  Debjani Chakraborty,et al.  A theoretical development on a fuzzy distance measure for fuzzy numbers , 2006, Math. Comput. Model..

[21]  Hans J. Zimmermann,et al.  Results of Empirical Studies in Fuzzy Set Theory , 1978 .

[22]  Samir Dani,et al.  Resilience: the concept, a literature review and future directions , 2011 .

[23]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[24]  Dobrila Petrovic.  Modelling and analysis of serial supply chains in uncertain environments , 1997 .

[25]  Taho Yang,et al.  Multiple-attribute decision making methods for plant layout design problem , 2007 .

[26]  Danijela Tadic,et al.  New integrated approach to the problem of ranking and supplier selection under uncertainties , 2011 .

[27]  Kaveh Khalili Damghani,et al.  Application of a fuzzy TOPSIS method base on modified preference ratio and fuzzy distance measurement in assessment of traffic police centers performance , 2010, Appl. Soft Comput..

[28]  Erica Seville,et al.  Developing a Tool to Measure and Compare Organizations’ Resilience , 2013 .

[29]  Reza Zanjirani Farahani,et al.  Fuzzy AHP to determine the relative weights of evaluation criteria and Fuzzy TOPSIS to rank the alternatives , 2010, Appl. Soft Comput..

[30]  D. Chang.  Applications of the extent analysis method on fuzzy AHP , 1996 .

[31]  Danijela Tadic,et al.  An assessment of organizational resilience potential in SMEs of the process industry, a fuzzy approach , 2013 .

[32]  Rick L. Wilson,et al.  Describing processing time when simulating JIT environments , 1992 .

[33]  H. Carter.  Fuzzy Sets and Systems — Theory and Applications , 1982 .

[34]  Dimitris Askounis,et al.  A new TOPSIS-based multi-criteria approach to personnel selection , 2010, Expert Syst. Appl..

[35]  Iraj Mahdavi,et al.  Designing a model of fuzzy TOPSIS in multiple criteria decision making , 2008, Appl. Math. Comput..

[36]  T. Saaty.  How to Make a Decision: The Analytic Hierarchy Process , 1990 .