An Integrated Picture Fuzzy Decision Support Framework for Next-Generation Nuclear Reactor Technology Selection

Abstract

The topic of selecting next generation nuclear reactors represents the multi-criteria decision-making problem which requires considering such conflicting criteria as safety, environment sustainability, economic efficiency and technological maturity. This paper proposes a multi-stage decision support process that combines expert-panel optimization, Picture Fuzzy Sets (PiFS) methodology and advanced techniques of MCDM. It includes five consecutive stages: (i) expert-panel optimization with the use of Hartigan–Wong algorithm for objective selection of 11 experts from 45 candidate-experts; (ii) aggregation of experts' opinions with the use of Picture Fuzzy Bonferroni Mean (PiFBM) operator to consider interaction effects between experts' assessments; (iii) criteria weights calculation with the use of Picture Fuzzy Simple Weight Calculation (PiF-SIWEC) technique; (iv) alternatives' ranking with the use of Picture Fuzzy Ranking of Alternatives with Weights of Criterion (PiF-RAWEC); (v) sensitivity and comparative analyses. The validity of the proposed decision-making framework is illustrated on the example of ranking five candidate reactor technologies small modular reactor, high temperature gas-cooled reactor, sodium-cooled fast reactor, molten salt reactor, advanced PWR based on eight criteria that include technical, economic, safety, environment and strategic aspects. The results were also compared to using six different Picture Fuzzy MCDM methods. The average value of Spearman rank correlation coefficient equals to 0.94, what indicates the stability of the results. According to PiF-SIWEC analysis, the most influential criterion is the Technical (38.70%), followed by Strategic (29.59%), Safety (17.60%), Environment (8.24%) and Economic (5.85%) aspects. The most important criterion is passive safety (C4) with the importance of 17.60%. The results of sensitivity analysis prove that the rankings are not sensitive to variation of criteria weight values up to 20%. Comparative analysis confirms the consistency of PiF-RAWEC with other methods of Picture Fuzzy sets. Three main contributions of the research are (i) introducing the systematic and data-driven approach for expert-panel selection with the help of clustering algorithms; (ii) presenting the novel application of PiF-SIWEC and PiF-RAWEC methods; (iii) providing the recommendations for nuclear technology planning in terms of the increasing role of long-term strategic advantages in the selection of nuclear reactors. The research offers the tool for decision-makers to make justified decisions in the difficult nuclear technology landscape during energy transition.