Abstract
The paper undertakes the problem of proper structuring of multi-criteria decision support models. To achieve that, a methodological framework is proposed. The authors’ framework is the basis for the relevance analysis of individual criteria in any considered decision model. The formal foundations of the authors’ approach provide a reference set of Multi-Criteria Decision Analysis (MCDA) methods (TOPSIS, VIKOR, COMET) along with their similarity coefficients (Spearman correlation coefficients and WS coefficient). In the empirical research, a practical MCDA-based wind farm location problem was studied. Reference rankings of the decision variants were obtained, followed by a set of rankings in which particular criteria were excluded. This was the basis for testing the similarity of the obtained solutions sets, as well as for recommendations in terms of both indicating the high significance and the possible elimination of individual criteria in the original model. When carrying out the analyzes, both the positions in the final rankings, as well as the corresponding values of utility functions of the decision variants were studied. As a result of the detailed analysis of the obtained results, recommendations were presented in the field of reference criteria set for the considered decision problem, thus demonstrating the practical usefulness of the authors’ proposed approach. It should be pointed out that the presented study of criteria relevance is an important factor for objectification of the multi-criteria decision support processes.
Highlights
In recent times, decision-making in the field of energy policy has been determined by technological and economic factors [1]
The results obtained with the VIKOR and COMET methods can be found in Appendices A and B
The results of using the VIKOR and COMET methods are included in Appendices C and D
Summary
Decision-making in the field of energy policy has been determined by technological and economic factors [1]. The transfer of the principles of sustainability to the energy sector results in the inclusion of important groups of pro-environmental factors in the decision-making process [2] and implies the assessment of future actions in the social dimension [3]. This results in the fact that planning or evaluation of energy policies for countries and regions becomes a complex process [4]. The current state of the art provides a solid justification for this fact by showing a strong potential of methods of multi-criteria decision support in the area of planning and evaluation of energy activities [7,8,9]
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