The use of improved TOPSIS method based on experimental design and Chebyshev regression in solving MCDM problems

Decision makers today are faced with a wide range of alternative options and a large set of conflicting criteria. How to make trade-off between these conflicting attributes and make a scientific decision is always a difficult task. Although a lot of multiple criteria decision making (MCDM) methods are available to deal with selection applications, it’s observed that in most of these methods the ranking results are very sensitive to the changes in the attribute weights. The calculation process is also ineffective when a new alternative is added or removed from the MCDM problem. This paper presents an improved TOPSIS method based on experimental design and Chebyshev orthogonal polynomial regression. A feature of this method is that it employs the experimental design technique to assign the attribute weights and uses Chebyshev regression to build a regression model. This model can help and guide a decision maker to make a reasonable judgment easily. The proposed methodology is particularized through an equipment selection problem in manufacturing environment. Two more illustrative examples are conducted to demonstrate the applicability of the proposed method. In all the cases, the results obtained using the proposed method almost corroborate with those derived by the earlier researchers which proves the validity, capability and potentiality of this method in solving real-life MCDM problems.

[1]  William G. Cochran,et al.  Experimental Designs, 2nd Edition , 1950 .

[2]  Bernard Roy,et al.  Classement et choix en présence de points de vue multiples , 1968 .

[3]  K R MacCrimmon,et al.  Decisionmaking among Multiple-Attribute Alternatives: A Survey and Consolidated Approach , 1968 .

[4]  T. J. Rivlin.  The Chebyshev polynomials , 1974 .

[5]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[6]  Jean Pierre Brans,et al.  HOW TO SELECT AND HOW TO RANK PROJECTS: THE PROMETHEE METHOD , 1986 .

[7]  田口 玄一,et al.  System of experimental design : engineering methods to optimize quality and minimize costs , 1987 .

[8]  Jyh-Cheng Yu,et al.  Computer-aided design for manufacturing process selection , 1993, J. Intell. Manuf..

[9]  Georges Fadel,et al.  Expert system-based selection of the preferred direction of build for rapid prototyping processes , 1995, J. Intell. Manuf..

[10]  T. R. Bement,et al.  Taguchi techniques for quality engineering , 1995 .

[11]  V. P. Dolgin.  Dynamic diagnostics of mechanical manufacturing systems , 1996 .

[12]  T. Saaty.  Fundamentals of Decision Making and Priority Theory With the Analytic Hierarchy Process , 2000 .

[13]  Ta-Chung Chu,et al.  Facility Location Selection Using Fuzzy TOPSIS Under Group Decisions , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[14]  Taho Yang,et al.  A hierarchical AHP/DEA methodology for the facilities layout design problem , 2003, Eur. J. Oper. Res..

[15]  Tung-Kuan Liu,et al.  Hybrid Taguchi-genetic algorithm for global numerical optimization , 2004, IEEE Transactions on Evolutionary Computation.

[16]  Mahmoud A. Abo-Sinna,et al.  Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems , 2005, Appl. Math. Comput..

[17]  A. Sudjianto,et al.  An Efficient Algorithm for Constructing Optimal Design of Computer Experiments , 2005, DAC 2003.

[18]  Edmundas Kazimieras Zavadskas,et al.  The MOORA method and its application to privatization in a transition economy , 2006 .

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

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

[21]  Taho Yang,et al.  The use of grey relational analysis in solving multiple attribute decision-making problems , 2008, Comput. Ind. Eng..

[22]  Metin Dagdeviren,et al.  Decision making in equipment selection: an integrated approach with AHP and PROMETHEE , 2008, J. Intell. Manuf..

[23]  Selin Soner Kara,et al.  A hybrid fuzzy MCDM approach to machine tool selection , 2008, J. Intell. Manuf..

[24]  Sifeng Liu,et al.  A DEA-TOPSIS method for multiple criteria decision analysis in emergency management , 2009 .

[25]  M. Yurdakul,et al.  Application of correlation test to criteria selection for multi criteria decision making (MCDM) models , 2009 .

[26]  Serkan Yavuz,et al.  Weapon selection using the AHP and TOPSIS methods under fuzzy environment , 2009, Expert Syst. Appl..

[27]  Fernando Casanova,et al.  Abrasive wear effect of sugarcane juice on sugarcane rolls , 2010 .

[28]  Jyrki Wallenius,et al.  Multiple Criteria Decision Making: From Early History to the 21st Century , 2011 .

[29]  R. Venkata Rao,et al.  Industrial robot selection using a novel decision making method considering objective and subjective preferences , 2011, Robotics Auton. Syst..

[30]  Shankar Chakraborty,et al.  Applications of the MOORA method for decision making in manufacturing environment , 2011 .

[31]  Subhash C. Mishra,et al.  CCorrosion behaviour of ferritic steel, austenitic steel and low carbon steel grades in sugarcane juice , 2012 .

[32]  S. Chakraborty,et al.  Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection , 2012 .

[33]  Morteza Yazdani,et al.  A state-of the-art survey of TOPSIS applications , 2012, Expert Syst. Appl..

[34]  José Ramón San Cristóbal,et al.  Multi Criteria Analysis in the Renewable Energy Industry , 2012 .

[35]  Sanjay Sharma,et al.  An integrative supplier selection model using Taguchi loss function, TOPSIS and multi criteria goal programming , 2013, J. Intell. Manuf..

[36]  R. Venkata Rao,et al.  Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods , 2013 .

[37]  Seyed Taghi Akhavan Niaki,et al.  A multi-objective facility location model with batch arrivals: two parameter-tuned meta-heuristic algorithms , 2013, J. Intell. Manuf..

[38]  Min Xu,et al.  Chebyshev fitting of complex surfaces for precision metrology , 2013 .

[39]  C E Emenonye,et al.  CHEBYSHEV’S Polynomial Approximation of an N-Warehouse Stock Allocation Model in Dynamic Programming , 2014 .

[40]  Nong Zhang,et al.  An interval uncertain optimization method for vehicle suspensions using Chebyshev metamodels , 2014 .

[41]  L. Anojkumar,et al.  Comparative analysis of MCDM methods for pipe material selection in sugar industry , 2014, Expert Syst. Appl..