Selection of the most appropriate contractor for the installation of solar panels is essential to maximizing the benefit of this renewable, sustainable energy source. Solar energy is one of the 100% renewable energy sources, but implementation may not be very simple and cost-effective. A key phase in the implementation of renewable energy is the evaluation of contractors for the installation of solar panels, which is addressed as a multi-criteria decision-making (MCDM) problem. A new hybrid method is proposed that combines the stepwise weight analysis ratio assessment (SWARA) and full consistent method (FUCOM) weights that are represented as grey numbers used with traditional grey relational analysis (GRA) and evaluation based on distance from average solution (EDAS) methods. The ranking of contractors by both methods is the same, which confirmed the results presented in this research. The use of the grey SWARA-FUCOM weighting method combined with the GRA and EDAS methods increased the decision-makers’ (DMs) confidence in awarding the installation of the solar panel energy system to the top-ranked contractor.

This article addresses the problem of subcontractor selection in an uncertain decision-making environment by proposing a new hybrid method and improving a classical multicriteria decision-making (MCDM) method. First, the proposed hybrid method combines subjective and objective weighting methods to estimate the weights of criteria that are represented as grey numbers. The subjective weighting method is the PA weighting method. The objective weighting method is the rank order centroid with slacks (ROCS) weighting method used to compensate for the limitation of the rank order centroid weighting method. Second, grey relational analysis (GRA) is improved by introducing both positive and negative reference (PNR) alternatives instead of a single reference alternative as in the classical GRA. Subsequently, the proposed hybrid grey-point allocation-ROCS weighting method and the GRA-PNR evaluation method are applied to select the most suitable subcontractor for the supply of heliostats for photothermal power station construction. Sensitivity analysis is conducted to verify the robustness of the results. Finally, the technique for order preferences by similarity to an ideal solution with grey values, simple additive weighting with grey relations, and additive ratio assessment with grey criteria scores is applied to validate the participation of the selected subcontractor in the project.

Project portfolio (PP) risk management entails making risk response decisions (RRDs) on single projects within PPs (SPPPs). However, the presence of uncertain project interdependencies (UPIs) increases the difficulty of RRDs. This study provides a decision model to make robust RRDs for SPPPs under UPIs, using a four-stage interval optimization model based on a two-tier risk-project network. A two-tier network is first constructed where the upper nodes denote the risks and the lower nodes denote the alternative projects. Next, a PP selection model is developed to obtain the probability of the projects being selected through simulation, in which the project interdependencies (PIs) are measured by the additional returns from the PP. Polytopic fuzzy linguistic term sets are formed to explore the strength of the relationships between the risks, and between the projects and risks. Third, the gravity model and the decision-making trial and evaluation laboratory are applied to prioritize the risks. Fourth, an interval optimization model under the uncertain response budget is constructed to yield the RRD outcome. The proposed model is validated through the RRDs for a Chinese construction firm. The results inform that, first, the mean utility of a decision maker (DM) considering UPIs fares better than those ignoring PIs and those considering PIs with certainty, second, the DM's mean utility considering PIs with certainty is still better than those ignoring PIs, and third, relying on the minimum budget threshold is more effective for reducing the uncertainty and improving the mean utility of a DM.

Evaluating the reputation of higher institutions is important as a benchmark for the continuous development of universities. In multi-criteria decision-making (MCDM), the estimation of the criteria weight is significant in improving the accuracy of the evaluation results. However, criteria weights assigned by decision makers (DMs) can be inaccurate when not measured from the orientations from which the DMs pursue their decision goals. This paper combines the grey system theory (GST) and regulatory focus theory (RFT) in estimating the weights of criteria by measuring the promotional and prevention focus orientations of the DMs and representing it as grey numbers. In this paper, we showed not all weight measurements from the DMs are symmetrical. In essence, to improve the accuracy of the MCDM weights, the grey regulatory focus theory (GRFT) weighting method is proposed to estimate the weights of an MCDM problem as well as aggregating the weights of the group DMs, thereby overcoming the limitation of a single orientation measurement. Furthermore, the GRFT weighting method is applied in ranking four universities in Xi’an city of China from a sample data from 1200 students. The most reputable university is consistent with the other ranking indexes in the literature.