Akademik Veri Yönetim Sistemi
IEEE Access, 2024 (SCI-Expanded)
Distance-based Multi-Criteria Decision Making (MCDM) methods offer a robust framework for evaluating and ranking alternatives amidst conflicting criteria. As a subset of multi-attribute decision making, distance-based methods in existing literature primarily focus on analyzing alternatives based on their distances to reference points, such as positive, average, or negative ideal solutions. However, literature lacks a technique where the reference or target point is the decision itself. Despite their conceptual simplicity, visual appeal, and flexibility, which make them valuable across various academic disciplines, these methods require meticulous selection of distance metrics and reference points. Moreover, there is a notable absence of vector-based multi-attribute decision making methods. Unlike distance-based approaches, vectors include both magnitude and direction, making them more adept at capturing dimensional positioning and similarity operations. To address this gap, this article introduces a novel vector-based MCDM methodology called the Vector-Based Preference Aided Ranking System (V-PARS). This methodology, grounded in Nudge Theory and preference ordering, positions the decisions themselves as the reference point, supported by approaches from Social Choice Theory, such as the Borda Count, Kemeny-Young approach, and Copeland method. It incorporates both objective and subjective data, including crisp and uncertain (e.g. fuzzy) data, and addresses normalization and scalization in MCDM procedures. The proposed framework is applied to evaluate micromobility vehicles within the context of Paris/France, aligned with the European Commission’s 2050 targets. The rise of micromobility solutions, such as e-scooters and e-bikes, has introduced additional complexity into urban transportation planning. Therefore, selecting suitable micromobility vehicles for specific contexts requires a systematic approach. The V-PARS methodology facilitates this process by identifying and ranking micromobility vehicles based on their suitability for different city districts, ultimately contributing to more informed and sustainable transportation decisions.