• Title of article

    Evaluation and selection of energy technologies using an integrated graph theory and analytic hierarchy process methods

  • Author/Authors

    Lanjewar، P. B. نويسنده Dept. of Mech. Engg., St. Vincent Pallotti College of Engg. & Technology, Nagpur, India , , Rao، R. V. نويسنده Dept. of Mech. Engg., S. V. National Institute of Technology, Surat, India , , Kale، A. V. نويسنده Dept. of Mech. Engg., Yashwantrao Chavan College of Engineering, Nagpur, India , , Taler، J. نويسنده Politechnika Krakowska, Instytut Maszyn i Urz?dze? Energetycznych, Al. Jana Paw?a II 37, 31-864 Krak?w, Poland , , Oc?o?، P. نويسنده Politechnika Krakowska, Instytut Maszyn i Urz?dze? Energetycznych, Al. Jana Paw?a II 37, 31-864 Krak?w, Poland ,

  • Issue Information
    فصلنامه با شماره پیاپی 16 سال 2016
  • Pages
    112
  • From page
    237
  • To page
    348
  • Abstract
    The evaluation and selection of energy technologies involve a large number of attributes whose selection and weighting is decided in accordance with the social, environmental, technical and economic framework. In the present work an integrated multiple attribute decision making methodology is developed by combining graph theory and analytic hierarchy process methods to deal with the evaluation and selection of energy technologies. The energy technology selection attributes digraph enables a quick visual appraisal of the energy technology selection attributes and their interrelationships. The preference index provides a total objective score for comparison of energy technologies alternatives. Application of matrix permanent offers a better appreciation of the considered attributes and helps to analyze the different alternatives from combinatorial viewpoint. The AHP is used to assign relative weights to the attributes. Four examples of evaluation and selection of energy technologies are considered in order to demonstrate and validate the proposed method.
  • Journal title
    Decision Science Letters
  • Serial Year
    2016
  • Journal title
    Decision Science Letters
  • Record number

    2355804