A comparative analysis of grey ranking approaches

Ranking and comparing grey numbers represent a very important decision-making procedure in any given grey environment. The purpose of this paper is to study the existing approaches of ordering interval grey numbers in the context of decision making by surveying existing definitions.,Different methods developed for comparing grey numbers are presented along with their disadvantages and advantages in terms of comparison outcomes. Practical examples are employed to show the importance and necessity of using appropriate methods to compare grey numbers.,Most the available methods are not suitable for pointing out which number is larger when the nuclei of the grey numbers of concern are the same. Also, these available methods are also considered in terms of partial order and total order. Kernel and degree of greyness of grey numbers method is more advantageous than other methods and almost eliminates the shortcomings of other methods.,Different methods for ranking grey numbers are presented where each of them has disadvantages and advantages. By using different methods, grey interval numbers are compared and the results show that some methods cannot make grey number comparisons in some situations. The authors intend to find a method that can compare grey numbers in any situation. The findings of this research can prevent errors that may occur based on inaccurate comparisons of grey numbers in decision making. There are various research studies on the comparison of grey numbers, but there is no research on the comparison of these methods and their disadvantages, advantages or their total or partial order.

[1]  Xi-zu Yan,et al.  The portfolio models of contained grey profit under uncertainty , 2014, Grey Syst. Theory Appl..

[2]  Nan Yao,et al.  A method of ranking interval numbers based on degrees for multiple attribute decision making , 2016, J. Intell. Fuzzy Syst..

[3]  Honghua Wu,et al.  Aggregation Operators of Interval Grey Numbers and Their Use in Grey Multi-Attribute Decision-Making , 2013 .

[4]  Pu Li,et al.  Chance constrained programming approach to process optimization under uncertainty , 2008, Comput. Chem. Eng..

[5]  Masahiro Inuiguchi,et al.  The usefulness of possibilistic programming in production planning problems , 1994 .

[6]  A. Charnes,et al.  Chance-Constrained Programming , 1959 .

[7]  Yong Liu,et al.  Ranking grey numbers based on dominance grey degrees , 2014 .

[8]  B. Hu,et al.  A novel approach in uncertain programming part I: new arithmetic and order relation for interval numbers , 2006 .

[9]  Edmundas Kazimieras Zavadskas,et al.  Multi-Attribute Decision-Making Model by Applying Grey Numbers , 2009, Informatica.

[10]  Amin Mahmoudi,et al.  A note on "a multi-objective programming approach to solve grey linear programming" , 2018, Grey Syst. Theory Appl..

[11]  Qiang Wu,et al.  Construction and Applicaton of Grey Concept Lattices , 2013, Informatica.

[12]  Naiming Xie,et al.  Interval grey numbers based multi-attribute decision making method for supplier selection , 2014, Kybernetes.

[13]  Morteza Bagherpour,et al.  Utility-Numbers Theory , 2019, IEEE Access.

[14]  Amin Mahmoudi,et al.  A grey mathematical model for crashing of projects by considering time, cost, quality, risk and law of diminishing returns , 2018, Grey Syst. Theory Appl..

[15]  Nikolaos V. Sahinidis,et al.  Optimization under uncertainty: state-of-the-art and opportunities , 2004, Comput. Chem. Eng..

[16]  Asoke Kumar Bhunia,et al.  A Comparative Study of Different Order Relations of Intervals , 2012, Reliab. Comput..

[17]  Amin Mahmoudi,et al.  Grey-fuzzy solution for multi-objective linear programming with interval coefficients , 2018, Grey Syst. Theory Appl..

[18]  Davood Darvishi,et al.  Planning livestock diet with fuzzy requirements , 2018 .

[19]  Naiming Xie,et al.  Novel methods on comparing grey numbers , 2010 .

[20]  Seyed Hossein Razavi Hajiagha,et al.  A multiobjective programming approach to solve grey linear programming , 2012, Grey Syst. Theory Appl..

[21]  Tapan Kumar Pal,et al.  On comparing interval numbers , 2000, Eur. J. Oper. Res..

[22]  D. Darvishi Salookolayi,et al.  Application Of Fuzzy Optimization In Diet Formulation , 2011 .

[23]  Sifeng Liu,et al.  Patients' satisfaction and public and private sectors' health care service quality in Pakistan: Application of grey decision analysis approaches , 2019, The International journal of health planning and management.

[24]  Peng Li,et al.  A new two-stage grey evaluation decision-making method for interval grey numbers , 2018, Kybernetes.

[25]  Sifeng Liu,et al.  Algorithm rules of interval grey numbers based on different "kernel" and the degree of greyness of grey numbers , 2017, Grey Syst. Theory Appl..

[26]  Efstratios N. Pistikopoulos,et al.  A novel flexibility analysis approach for processes with stochastic parameters , 1990 .

[27]  Deng Ju-Long,et al.  Control problems of grey systems , 1982 .

[28]  Amin Mahmoudi,et al.  A novel method for solving linear programming with grey parameters , 2019, J. Intell. Fuzzy Syst..

[29]  Yi Lin,et al.  Theory of grey systems: capturing uncertainties of grey information , 2004 .

[30]  Gilbert Laporte,et al.  A Priori Optimization of the Probabilistic Traveling Salesman Problem , 1994, Oper. Res..

[31]  Debjani Chakraborty,et al.  Interpretation of inequality constraints involving interval coefficients and a solution to interval linear programming , 2001, Fuzzy Sets Syst..

[32]  Amin Mahmoudi,et al.  Suitable computerized maintenance management system selection using grey group TOPSIS and fuzzy group VIKOR: A case study , 2018 .

[33]  Qiaoxing Li,et al.  Review paper: A Briefing to Grey Systems Theory , 2014 .

[34]  Zheng-Xin Wang,et al.  Correlation analysis of sequences with interval grey numbers based on the kernel and greyness degree , 2013, Kybernetes.

[35]  Sifeng Liu,et al.  A new approach in animal diet using grey system theory , 2018, Grey Syst. Theory Appl..

[36]  Efstratios N. Pistikopoulos,et al.  Stochastic optimization based algorithms for process synthesis under uncertainty , 1998 .

[37]  Robert Ivor John,et al.  Grey sets and greyness , 2012, Inf. Sci..

[38]  Ozan Çakır On visualizing the number comparison scheme in grey extent analysis , 2013, Kybernetes.

[39]  GuoDong Li,et al.  A grey-based decision-making approach to the supplier selection problem , 2007, Math. Comput. Model..

[40]  H. Ishibuchi,et al.  Multiobjective programming in optimization of the interval objective function , 1990 .