4.5 Article

Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives

Journal

Publisher

SPRINGER
DOI: 10.1007/s13762-016-0982-7

Keywords

Multi-criteria decision making; Linguistic labels; TOPSIS; Qualitative reasoning; Energy planning

Funding

  1. SENSORIAL research Project [TIN2010-20966-C02-01, TIN2010-20966-C02-02]
  2. Spanish Ministry of Science and Information Technology
  3. ESADE Business School
  4. Ramon Llull University

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Multi-criteria decision-making methods support decision makers in all stages of the decision-making process by providing useful data. However, criteria are not always certain as uncertainty is a feature of the real world. MCDM methods under uncertainty and fuzzy systems are accepted as suitable techniques in conflicting problems that cannot be represented by numerical values, in particular in energy analysis and planning. In this paper, a modified TOPSIS method for multi-criteria group decision-making with qualitative linguistic labels is proposed. This method addresses uncertainty considering different levels of precision. Each decision maker's judgment on the performance of alternatives with respect to each criterion is expressed by qualitative linguistic labels. The new method takes into account linguistic data provided by the decision makers without any previous aggregation. Decision maker judgments are incorporated into the proposed method to generate a complete ranking of alternatives. An application in energy planning is presented as an illustrative case example in which energy policy alternatives are ranked. Seven energy alternatives under nine criteria were evaluated according to the opinion of three environmental and energy experts. The weights of the criteria are determined by fuzzy AHP, and the alternatives are ranked using qualitative TOPSIS. The proposed approach is compared with a modified fuzzy TOPSIS method, showing the advantages of the proposed approach when dealing with linguistic assessments to model uncertainty and imprecision. Although the new approach requires less cognitive effort to decision makers, it yields similar results.

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