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.

Engineering and technological breakthroughs in sustainability play a crucial role in reducing carbon emissions. An important aspect of this is the active participation of enterprises in addressing carbon reduction as a systemic approach. In response to government incentives in the People’s Republic of China, Chinese enterprises have developed carbon reduction systems to align their organizational goals with national long-term plans. This paper evaluates the carbon reduction schemes employed by six companies as a multi-criteria decision-making (MCDM) problem. To this end, we propose a new hybrid MCDM method called the grey-MEREC-MAIRCA method. This method combines the recently developed method based on the removal effects of criteria (MEREC) for weighting and multi-attribute ideal-real comparative analysis (MAIRCA) based on the grey system theory. The proposed hybrid method provides the additional benefit of accounting for uncertainty in decision making. Notable findings of this research, based on the decision-maker scores, are that the control of direct carbon emissions and energy-saving efficiency are top priorities. In contrast, committing to corporate social responsibility through carbon public welfare and information disclosure are considered lesser priorities. Furthermore, the ranking results obtained using this method are compared with those from the classical weighted sum model and the technique for order preference by similarity to ideal solution (TOPSIS), confirming the selection of the best company. Despite the limitation of the proposed method and the additional steps needed in the evaluation, it opens up opportunities for future research to develop simpler MCDM methods under uncertainty.

As West Africa investments grow, the decision in which country to begin investment is of great importance to investors. The complexity of the criteria involved draws us to use a Multicriteria Decision-Making (MCDM) approach to address this problem. In this paper, we use grey numbers in representing ranges of data and propose Grey Weighted Sum Model (GWSM) for evaluating and ranking of alternatives. Sensitivity analysis is carried out considering wide ranges of uncertainties to verify the changes that can affect the results. The Gambia is ranked the best country in West Africa. The GWSM is highly recommended for long-term investors because GWSM considers the uncertainty of a business environment over a period of years. Finally, GWSM can be used in conjunction with various weighting techniques putting the preferences of the investors into consideration.

As the Chinese economy becomes stronger and stronger, there is increasing interest by some countries to adopt the Chinese approach to grow. The outstanding Chinese growth can be partly attributed to the education quality that is obtainable from the universities in China. The need to evaluate the reputation of Chinese universities is essential. In this paper, a hierarchical model for evaluating universities is proposed. Also, the Integral Linear Programming with Grey Possibility (ILP-GP) method is presented, which does not require the assumption of 0.5 for the grey distinguishing coefficient (ζ=0.5) in Grey Relational Analysis (GRA). In this paper, a numerical example for evaluating the reputation of five universities based on the sample data from 1,565 students in Shaanxi Province, China, is presented, and the most reputable university in this research is consistent with rankings in the literature.