Selection of Industrial Robots for Automation Applications in Multiple Attribute Decision Making Environment using the Analytical Network Process

  • Authors

    • N Rishi Kanth
    • A Srinath
    • J Suresh Kumar
    2018-07-20
    https://doi.org/10.14419/ijet.v7i3.12.16114
  • Analytical Network process (ANP), AHP, Robot selection, priority vector, super matrix, sensitivity analysis.
  • Abstract

    Analytical Network process (ANP), is applied here as a decision making technique for the selection of appropriate robots for industrial and automation applications. The core motivation of applying, in particular, the ANP technique is that robot selection is dependent upon a number of attributes and criteria which have strong influences/interdependencies upon each other. The ANP, as a multiple attribute decision making (MADM) technique for robot selection, captures the effects of these cross hierarchical dependencies, and appropriately maps the influences within the clusters and between the various alternatives. Simultaneously, the technique does not include the assumption of independence of higher-level elements from lower level elements and about the independence of the elements within a level. First, a set of attributes, which influence the selection of the robots, are identified. Next, using the various steps of ANP, viz., pair wise comparisons matrices and priority vectors determination and the development of the super-matrix the global weights of the attributes with respect to other attributes are determined. The final alternatives are then rated as per the graduated weights of the respective attributes. Thus, a comprehensive solution towards selection of robots enabling the decision-makers to suitably understand the complex relationships of the relevant qualitative and quantitative attributes in the decision-making is obtained. The technique is also illustrated using detailed analysis for a specific case of decision making between three robot suppliers and selection of appropriate robot from alternatives. In order to get more insight into relationships among various attributes and their effect on decision makers, the sensitivity analysis of the results with respect to determinant level attributes is carried out.

     

     

     

  • References

    1. [1] R. Hinson, “Robotics-environment a major factor in robot selection,†Industrial Engineering, vol. 15 (10), 1983, pp. 32-30.

      [2] R. Hinson, “Knowing work envelops helps in evaluating robots,†Industrial Engineering, vol. 15 (7), 1983, pp. 22-27.

      [3] R. C. Dorf, and S. Y. Nof, Int. Encyclopedia of Robotics Applications and Automation, John Wiley & Sons, Inc., New York, 1988.

      [4] E. I. Rivin, Mechanical Design of Robots, McGraw Hill, NY, 1988.

      [5] T. Yoshikawa, Foundations of Robotics Analysis and Control, Presntice-Hall of India Pvt. Ltd., New Delhi, 1998.

      [6] K. Knott, and R. D. Getto, “A model for evaluating alternative robot systems under uncertainty,†Int J Production Research 1982;20:155–65.

      [7] DR Towill, “A Production Engineering Approach to Robot Selection,†Omega Int. J. of Mgmt Sci., Vol. 12, No. 3, pp. 261 272, 1984.

      [8] Bartholomew O. Nnaji and M. Yannacopoulou, “A utility theory based robot selection and evaluation for electronics assembly,†Computers ind. Engng Vol. 14, No. 4, pp. 477-493, 1988

      [9] M. M. Imang and R. J. Schlesinger, “Decision models for robot selection: a comparison of ordinary least squares and linear goal programming methodâ€, Decision Science, 20, pp. 40–53, 1989.

      [10] N. Boubekri, M. Sahoui and C. Lakrib, “Development of an expert system for industrial robot selection,†Computational Industrial Engineering 1991;20:119–27.

      [11] Chao-Yen Wu, “Robot selection decision support system: a fuzzy set approach,†Math/ Comput. Mafelling, Vol. 14, pp. 440-443. 1990,

      [12] M. J. J. Wang,, “ A decision support system for robot selection,†Decision Support Systems 7 (1991) 273-283

      [13] V. P. Agrawal, V. Kohli, and S. Gupta, “Computer aided robot selection: the ‘multiple attribute decision making’ approach,†Int J Production research 1991; 29:1629–44.

      [14] R. Venkata Rao and K.K. Padmanabhan, “Selection, identification and comparison of industrial robots using digraph and matrix methods,†Robotics and Computer-Integrated Manufacturing 22 (2006) 373–383.

  • Downloads

  • How to Cite

    Rishi Kanth, N., Srinath, A., & Suresh Kumar, J. (2018). Selection of Industrial Robots for Automation Applications in Multiple Attribute Decision Making Environment using the Analytical Network Process. International Journal of Engineering & Technology, 7(3.12), 392-402. https://doi.org/10.14419/ijet.v7i3.12.16114

    Received date: 2018-07-23

    Accepted date: 2018-07-23

    Published date: 2018-07-20