Modern consumer society uses an increasing number of products to meet its needs, which become waste after use, thus posing a serious problem that threatens sustainable development. Investment in waste recycling, due to a high level of non-financial benefits, is considered sustainable, especially in the End-of-life Vehicles (ELV) and Waste Electrical and Electronic Equipment (WEEE) recycling areas. The research objective of this paper is to test the sensitivity of the model for sustainable management of recycling projects by applying a cost-benefit analysis (CBA) to investment projects of car and refrigerator recycling in the Republic of Serbia. By testing the key risk factors of the above investment projects within the sensitivity analysis, the main aim is to determine the critical value of these variables in terms of the financial and social acceptability of these investment alternatives. The results obtained indicate that state subsidies have the greatest influence on defining the model of sustainable investment, especially in the field of e-waste recycling. The impact of other factors, the price of secondary raw materials and the social cost of CO2 emissions, is significantly smaller, but should certainly be taken into account when defining the optimal model of sustainable investment.
Using the strength of a single-valued neutrosophic set (SVNS) with the flexibility of a hesitant fuzzy set (HFS) yields a robust model named the single-valued neutrosophic hesitant fuzzy set (SVNHFS). Due to the ability to utilize three independent indexes (truthness, indeterminacy, and falsity), an SVNHFS is an efficient model for optimization and computational intelligence (CI) as well as an intelligent decision support system (IDSS). Taking advantage of the flexibility of operational parameters in Dombi’s t-norm and t-conorm operations, new aggregation operators (AOs) are proposed, which are named the SVN fuzzy Dombi weighted averaging (SVNHFDWA) operator, SVN hesitant fuzzy Dombi ordered weighted averaging (SVNHFDOWA) operator, SVN hesitant fuzzy Dombi hybrid averaging (SVNHFDHWA) operator, SVN hesitant fuzzy Dombi weighted geometric (SVNHFDWG) operator, SVN hesitant fuzzy Dombi ordered weighted geometric (SVNHFDOWG) operator as well as SVN hesitant fuzzy Dombi hybrid weighted geometric (SVNHFDHWG) operator. The efficiency of these AOs is investigated in order to determine the best option using SVN hesitant fuzzy numbers (SVNHFNs) in an IDSS. Additionally, a practical application of SVNHFDWA and SVNHFDWG is also presented to examine symmetrical analysis in the selection of wireless charging station for vehicles.
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