Neural Computing and Applications

, Volume 31, Issue 10, pp 6733–6746 | Cite as

Predictive mathematical model for solving multi-criteria decision-making problems

  • N. DeepaEmail author
  • K. Ganesan
  • Balaji Sethuramasamyraja
Original Article


In this paper, a predictive mathematical model is proposed to identify the best alternatives from the given set of alternatives characterized by multiple criteria. An objective function is developed to find the ranking index of the alternatives. A new Comprehensive-Technique for Order Preference by Similarity to Ideal Solution (C-TOPSIS) method is proposed which combines the comprehensive weights of the criteria with TOPSIS method. The proposed predictive mathematical model generates a ranking of the alternatives. An experimental study has been carried out by taking agricultural data set of rice paddy crop to demonstrate and validate the developed model. The results show significant correlation between the ranks obtained by the proposed model and the ranks obtained from the average yield per hectare. Also the results of the proposed method outperform the results of the other ranking methods, namely VIKOR and ELECTRE, particularly in the real world example. Thus, the developed predictive mathematical model seems to provide better results for the given alternatives and can also be used for other decision-making problems.


TOPSIS Ranking Objective Subjective Comprehensive Rank sum Grey relational Mean square weight method 



This work forms part of the R and D activities of TIFAC-CORE in Automotive Infotronics located at VIT University, Vellore. The authors would like to thank DST, Government of India, for providing necessary hardware and software support for completing this work successfully.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Velasquez M, Hester PT (2013) An analysis of multi-criteria decision making methods. Int J Oper Res 10(2):56–66MathSciNetGoogle Scholar
  2. 2.
    Roszkowska E (2011) Multi-criteria decision making models by applying the TOPSIS method to crisp and interval data. Mult Criteria Decis Mak Univ Econ Katow 6:200–230Google Scholar
  3. 3.
    Zardari NH, Ahmed K, Shirazi SM, Yusop ZB (2015) Literature review. In: Weighting methods and their effects on multi-criteria decision making model outcomes in water resources management. Springerbriefs in water science and technology. Springer, ChamGoogle Scholar
  4. 4.
    Bojorquez-Tapia LA, Diaz-Mondragon S, Ezcurra E (2001) GIS-based approach for participatory decision making and land suitability assessment. Int J Geogr Inf Sci 15(2):129–151CrossRefGoogle Scholar
  5. 5.
    Mustafa AA, Singh M, Sahoo RN, Ahmed N, Khanna M, Sarangi A, Mishra AK (2011) Land suitability analysis for different crops: a multi criteria decision making approach using remote sensing and GIS. Researcher 3(12):1–24Google Scholar
  6. 6.
    Wu HY, Chen JK, Chen IS, Zhuo HH (2012) Ranking universities based on performance evaluation by a hybrid MCDM model. Measurement 45(5):856–880CrossRefGoogle Scholar
  7. 7.
    Ma J, Fan ZP, Huang LH (1999) A subjective and objective integrated approach to determine attribute weights. Eur J Oper Res 112(2):397–404CrossRefGoogle Scholar
  8. 8.
    Shemshadi A, Shirazi H, Toreihi M, Tarokh MJ (2011) A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Syst Appl 38(10):12160–12167CrossRefGoogle Scholar
  9. 9.
    Rong Z, Sifeng L, Bin L (2009) A method for weight assignment by dominance-based rough sets approach. In: Control and decision conference, 2009 (CCDC’09) Chinese. IEEE, pp 6060–6065Google Scholar
  10. 10.
    Deepa N, Ganesan K (2016) Multi-class classification using hybrid soft decision model for agriculture crop selection. Neural Comput Appl.
  11. 11.
    Deepa N, Ganesan K (2017) Decision-making tool for crop selection for agriculture development. Neural Comput Appl.
  12. 12.
    Huang MJ, Zhang YB, Luo JH, Nie H (2015) Evaluation of economics journals based on reduction algorithm of rough set and grey correlation. J Manag Sustain 5(1):140Google Scholar
  13. 13.
    Mahalakshmi P, Ganesan K, Venkatasubramanian V (2012) DMTIOLA: decision making tool for identification of optimal location for aquaculture farming development. Aquacult Int 20(5):911–925CrossRefGoogle Scholar
  14. 14.
    Roszkowska Ewa (2013) Rank ordering criteria weighting methods: a comparative overview. Optim Stud Ekon Nr 5(65):14–33CrossRefGoogle Scholar
  15. 15.
    Hongjiu L, Yanrong H (2015) An evaluating method with combined assigning-weight based on maximizing variance. Sci Program 2015:3Google Scholar
  16. 16.
    Xuan L, Jinwei L, Mengxin X, Guowei H, Changming L (2015) Application of combination weighting grey correlation model in the optimization for deep foundation pit supporting scheme. Electron J Geotech Eng 20:6915–6926Google Scholar
  17. 17.
    Liu D, Zhao X (2013) Method and application for dynamic comprehensive evaluation with subjective and objective information. PLoS ONE 8(12):e83323CrossRefGoogle Scholar
  18. 18.
    Özcan T, Çelebi N, Esnaf Ş (2011) Comparative analysis of multi-criteria decision making methodologies and implementation of a warehouse location selection problem. Expert Syst Appl 38(8):9773–9779CrossRefGoogle Scholar
  19. 19.
    Hwang CL, Yoon K (2012) Multiple attribute decision making: methods and applications a state-of-the-art survey, vol 186. Springer, BerlinzbMATHGoogle Scholar
  20. 20.
    Stagnitti F, Austin C (1998) DESTA: a software tool for selecting sites for new aquaculture facilities. Aquacult Eng 18(2):79–93CrossRefGoogle Scholar
  21. 21.
    Bansiya J, Davis CG (2002) A hierarchical model for object-oriented design quality assessment. IEEE Trans Softw Eng 28(1):4–17CrossRefGoogle Scholar
  22. 22.
    Zar JH (1972) Significance testing of the Spearman rank correlation coefficient. J Am Stat Assoc 67(339):578–580CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.School of Information Technology and EngineeringVIT UniversityVelloreIndia
  2. 2.School of Information Technology and EngineeringVIT UniversityVelloreIndia
  3. 3.Department of Industrial Technology, Jordan College of Agricultural Sciences and TechnologyCalifornia State UniversityFresnoUSA

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