The main objective of this research is to give an overview of the Analytic Hierarchy Process (AHP) in neutrosophic environment. In some realistic situations, the decision makers might be unable to assign deterministic evaluation values to the comparison judgments due to his/her limited knowledge or the differences of individual judgments in group decision making. To overcome these challenges, we have used neutrosophic set theory to handle the AHP, where each pair-wise comparison judgment is represented as a triangular neutrosophic number (TNN). In this paper, neutrosophic theory is used to form AHP decision-making model for choosing the best candidates among the applications. A real life example is developed based on expert opinions from Zagazig University, Egypt. The problem is solved to show the effectiveness of the proposed neutrosophic-AHP decision making model.

Alternative fuel vehicles (AFVs) offer opportunities to lower fuel costs as well as to reduce greenhouse gas emissions, and, therefore, they are a feasible option for customers in the market. Due to technological advancements, decisions about suitable alternative fuel vehicles are a challenging problem for fleet operators. This paper aims to introduce a multi-attribute decision-analysis framework to rank and select the “alternative fuel vehicles (AFVs)” for a private home healthcare service provider in Chandigarh, India. The selection of AFVs can be treated as a decision-making problem, because of the presence of various qualitative and quantitative attributes. Thus, the current work introduces an integrated decision-making framework based on intuitionistic fuzzy-“method based on the removal effects of criteria (MEREC)”, “ranking sum (RS)”, and the “double normalization-based multi-aggregation (DNMA)” framework for assessing the AFVs. The combination of MEREC and RS is applied to assess the objective and subjective weighting values of various parameters for AFV assessment. The DNMA approach is utilized to prioritize the different AFVs over various significant parameters. According to the outcomes, the most significant parameters for AFV assessment are social benefits, fueling/charging infrastructure, and financial incentives, respectively. In this context, globally existing AFVs for the sustainable transportation sector are identified, and then prioritized against fifteen different criteria relevant to the environmental, economic, technological, social, and political aspects of sustainability. It is distinguished that electric vehicles (G₂), hybrid electric vehicles (G₁), and hydrogen vehicles (G₃) achieve higher overall performance compared to the other technologies available in India. The assessment outcomes prove that electric vehicles can serve as a valuable alternative for decreasing carbon emissions and negative effects on the environment. This technology contributes to transportation sector development and job creation in less developed areas of the country. Moreover, a comparison with existing studies and a sensitivity investigation are conferred to reveal the robustness and stability of the developed framework.

In this paper, globally existing low-carbon tourism strategies (LCTSs) are recognized and then ranked over fourteen different indicators relevant to SEE analysis (social (S), economic (E), and environmental (E) aspects) of sustainability. A comprehensive framework is proposed in which decision experts (DEs) are capable to assess linguistic values to give their decisions and contribute in the decision-making needs to rank the SEE aspects that affect the sustainable perspective of LCTSs. This paper therefore proposes an integrated decision-making framework considering the various conflicting indicators and SEE aspects of sustainability. In addition, treating of uncertainty and inconsistency for data, we consider a neutrosophic setting with the use of single-valued neutrosophic numbers (SVNNs). First, the MEREC (Method based on the removal effects of criteria) weighting procedure is applied to recognize the relative significance of the SEE aspects and their indicators. Second, the generalized Dombi operators are proposed and their elegant properties are discussed to obtain aggregated information of SVNNs. Third, MULTIMOORA method is used to prioritize alternatives. A case study considering six LCTSs is taken to approve the practicality of the introduced methodology, and comparison discussion is made to illustrate the benefits of the developed methodology. Sensitivity investigation is done to evidence the rationality and permanence of the proposed methodology with variations in indicators’ weights. The outcomes of the study offer valuable facts for low-carbon tourism experts and the outcomes of the case study specify that the LCTS-I is the optimum sustainable LCTSs with overall assessment degree of 0.208 after that LCTS-II with utility degree of 0.172.

Healthcare waste (HCW) management is an intricate issue upon which numerous factors, such as technical, economic, environmental, and social factors, have an impact. A determination on the best treatment method for HCW management can be viewed as a challenging multi-criteria decision-making (MCDM) problem in which various options and evaluation criteria are considered. One critical concern when assessing HCW treatment (HCWT) methods is the representation and treatment of dubious data. In this paper, we present a q-rung orthopair fuzzy full consistency method double normalization-based multi-aggregation methodology called q-ROF-FUCOM-DNMA to solve MCDM problems with q-rung orthopair fuzzy information (q-ROFI). In the proposed approach, criteria weights are estimated through the full consistency method (FUCOM) and a ranking of the alternatives is obtained through the double-normalization-based multi-aggregation (DNMA) method with q-ROFI. A HCWT method assessment issue was considered in order to clarify the relevance of the proposed approach. Five HCWT methods, including chemical disinfection, microwave disinfection, incineration, autoclaving (steam sterilization), and reverse polymerization, were considered as alternatives. The results show that autoclaving (steam sterilization) is the most efficient HCWT method. Furthermore, we performed a sensitivity analysis to determine the stability of the proposed approach. Additionally, we compared the presented approach with existing methods.

Electric vehicles (EVs) have achieved a great deal of success, indicating that the motor industry will soon be emission-free. They run on electricity stored in batteries, which their drivers can recharge using an external source of electricity. Therefore, the development of an infrastructure for charging EVs has become a necessity. In this paper, a real case study in Zagazig, Egypt, for evaluating, operating, and determining the most sustainable location for an EV charging station is presented. Identifying such a location is a complex task that requires considering many influencing factors and their sub-factors. Consequently, a general evaluation system that includes six key factors, in addition to nineteen sub-factors, has been developed. A multi-criteria decision-making (MCDM) approach is applied to deal with this problem. The neutrosophic theory with type-2 neutrosophic numbers (T2NNs) and a type-2 neutrosophic number weighted averaging (T2NNWA) operator are used to combine expert and specialized views since they are ambiguous and uncertain. Firstly, the Decision-making Trial and Evaluation Laboratory (DEMATEL) method is applied to prioritize key factors and sub-factors. Then, the Complex Proportional Assessment (COPRAS) technique selects six sustainable sites for EV charging stations. Also, comparison and sensitivity evaluations verify the approach's reliability, strength, stability, and performance. Finally, this article provides good information for policymakers and the results of the case study indicate that the economic factor is the most important factor in selecting the optimal location followed by the technical factor.