Achieving sustainability while considering various sociological and technological factors represents a challenging task. Through this research, it is necessary to determine the feasibility of electrifying the vehicle fleet of a local utility company and its intention to place primary emphasis on the use of clean technologies and the achievement of greater technological sustainability. The aim of the research is to identify the most suitable electric vehicle for the collection and transport of non-hazardous municipal waste by applying a multi-criteria model, as support for the transition to carbon-neutral transport. The main methodological contribution of the paper is the development of a new STENOZA (STEvic NOvarlic ZAvadskas) method for the evaluation and ranking of potential solutions. The evaluation and selection of the electric vehicle were carried out using three MCDM (Multi-Criteria Decision-Making) methods: MEREC (MEthod Based on the Removal Effects of Criteria), FUCOM (FUll COnsistency Method), and STENOZA. The objective-subjective MEREC-FUCOM model was applied to determine the weights of nine influential criteria, while STENOZA was used to evaluate and rank the electric vehicles. The results indicate that EV2 is the best option, regardless of any changes in conditions, as confirmed by verification tests and additional analyses conducted in this paper.

Multi-criteria decision-making methods conventionally rank alternatives by aggregating normalized criterion values into a composite score, an approach that depends on the normalization scheme, requires externally determined criterion weights, and evaluates performance in absolute terms without accounting for inter-criteria dependencies. This paper proposes machine learning-based Peer-Prediction Trees for multi-criteria decision-making, a novel method that ranks alternatives by their excess performance over data-driven peer expectations. For each criterion, a leave-one-out cross-validated decision tree predicts each alternative’s value from its performance on all other criteria; the residual between observed and predicted values extracts how much the alternative outperforms the empirical trade-off structure constraining its peers. Residuals are standardized, aligned with the preferred criterion direction, and aggregated into an Excess Performance Index using uniqueness-based weights derived endogenously from the cross-validated predictive fit. The method is validated on three real-world datasets: a numeric vehicle selection problem (22 alternatives, five criteria), a fully linguistic financial performance evaluation of Turkish listed firms (32 alternatives, four criteria), and a mixed numeric-linguistic exchange-traded fund selection problem (12 alternatives, nine criteria). The contributions of this work are fourfold: (1) the introduction of peer prediction via leave-one-out cross-validated decision trees as a multi-criteria decision-making ranking paradigm that evaluates excess performance rather than absolute scores; (2) a uniqueness-based criterion weighting scheme embedded into the method, eliminating the need for separate weighting procedures; (3) native handling of numeric, linguistic, and mixed decision matrices within a single unified framework; and (4) empirical validation across three heterogeneous real-world applications demonstrating the method’s versatility and interpretability.

Human resource management today, in an era of rapid technological achievements and a changing work environment, represents a great challenge. Particular attention is being directed toward the digitalization of business operations and technically modern management, while human resources still remain in the background. Teamwork, adaptability to new circumstances and quick response to problem-solving are the central part of this paper. The aim of the paper is to conduct an integrated performance evaluation of utility vehicle crews that collect and transport waste in the city of Doboj (Bosnia and Herzegovina) on a daily basis, using multi-criteria decision-making (MCDM) methods. Five crews (each crew consists of a driver and two support workers) work daily to keep the city clean while properly disposing of municipal waste. The selection of the best-ranked utility vehicle crew for the month of March 2026 was carried out using the FUCOM (Full Consistency Method) and MARCOS (Measurement Alternatives and Ranking according to COmpromise Solution) methods. The FUCOM method was used to determine the weights of nine influential criteria, while the MARCOS method was applied to rank five alternatives of utility vehicle crews. The research results showed that alternative A3 was ranked as the best-rated crew in the observed period, achieving the highest value of the utility function f(K3) = 0.878. The value f(K4) = 0.433 belongs to the worst alternative (A4) and represents the lower reference value within the MARCOS model.

This paper evaluates a hybrid institutional model for governing international transit highways in Georgia, with the Rikoti Pass section of the E-60 serving as a policy simulation case. The study combines institutional economics, public-choice theory, and transport economics to examine what the authors term “the free-access paradox”: when a capacity-constrained road is priced at zero, individual use decisions do not reflect the congestion and infrastructure costs imposed on other users and the public sector. The methodology is a transparent, parameterized mathematical simulation rather than an econometric estimation. It integrates a Bureau of Public Roads travel-time function, an endogenous short-run demand response and a social-welfare objective that treats toll revenue as a fiscal transfer rather than a social benefit. Under the stated reference assumptions, the fleet-average welfare-maximizing equivalent toll is approximately 44.04 GEL per vehicle. Meanwhile, premium-corridor demand declines from 12,000 to approximately 10,027 vehicles per day, and modeled operational welfare increases by about 34,185 GEL per day, or 2.83% relative to the free-access benchmark. The estimate excludes unverified fixed implementation costs and does not explicitly model congestion on the toll-free historical route. These results support a staged data-validation and pilot process, not the immediate adoption of a specific tariff. The paper’s institutional contribution is the integration of differentiated charging, public-private partnership governance, intelligent tolling, and a safeguarded free alternative within a single policy framework.

This study aims to develop a novel Multi Criteria Decision Making (MCDM) methodological model for use as a decision support tool in industrial machine selection problems. To this end, a new integrated MCDM model consisting of Logarithmic normalization and Standard Deviation (LOGSTA), Logarithmic Percentage Change-driven Objective Weighting (LOPCOW), Logarithmic Decomposition of Criteria Importance (LODECI), and Evaluation by Distance from Ideal Solution of Alternatives (EDISA) methods has been designed for a real-world lathe selection problem faced by a manufacturing company in Turkiye. The criterion weights obtained according to the three methods (LOGSTA, LOPCOW, and LODECI) were combined, and the combined criterion weights were transferred to the EDISA method to create an alternative lathe ranking. According to the combined criterion weights, the criterion with the highest importance level was failure frequency (C1), while the criterion with the lowest importance level was active working time (C5). According to the ranking obtained as a result of transferring the combined criterion weights to the EDISA method, the lathe with the highest performance was determined to be Doosan PUMA VT 900 (A2), while the lathe with the lowest performance was determined to be Doosan Puma 300 LM (A1). The comparison analysis shows that the EDISA method produces the same rankings as the ARAS, COPRAS, and RAWEC methods. It is believed that this framework enables manufacturing managers to compare acquisition cost, reliability, operating characteristics, and resale value within a transparent decision process.