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With the recent rising number of personal drones, the market is growing with more and more new drones. The actual number of alternatives is unsuspectedly huge. Nevertheless, each year their characteristics are only improving, which only complicate the selection. The manual optimal choice is a significant challenge, likely even impossible. Still, the most efficient way to estimate the ranking of the alternatives is a multi-criteria decision analysis (MCDA). In this paper, we use MCDA methods towards objective multi-criteria drone evaluation. Our work is based on VIKOR and COMET method which have presented their efficiency in many works with multi-criteria problems. For optimal selection on twelve alternatives of drones, nine criteria were used and two MCDA methods with the same weights. Finally, the result is presented and discussed in the end. [ABSTRACT FROM AUTHOR]

This paper aims to present an innovative approach based on two newly developed Multi-Criteria Decision-Making (MCDM) methods: COMET combined with TOPSIS and SPOTIS, which could be the basis for a decision support system (DSS) in the problem of selecting solar panels. Solar energy is one of the most promising and environmentally friendly energy sources because of the enormous potential of directly converting available solar radiation everywhere into electricity. Furthermore, ever-lower prices for photovoltaic systems make solar electricity more competitive with power from conventional energy sources, increasing interest in solar panels among companies and households. This fact generates the need for a user-friendly, objective, fully automated DSS to support the multi-criteria selection of solar panels. Both MCDM methods chosen for this purpose are rank-reversal-free and precise. First, the objective entropy weighting method was applied for determining criteria weights. Final rankings were compared by two ranking correlation coefficients: symmetrical r w and asymmetrical W S . Then the sensitivity analysis providing local weights of alternatives for decision criteria was performed. The obtained results prove the adequacy and practical usefulness of the presented approach in solving the problem of solar panels selection. [ABSTRACT FROM AUTHOR]