On the Integration of User Preferences by Using a Hybrid Methodology for Multi-Criteria Decision Making

Abstract

The evolution of smart cities depends on the effective integration of advanced technologies within urban environments. It necessitates a profound and nuanced analysis of urban residents’ aspirations and perceptions called User Preferences (UPs) concerning the Quality of Service (QoS) provision, both in its current state and in envisaged improvements across diverse sectors. This paper introduces a novel, hybrid methodology designed to interpret and evaluate residents’ feedback in the context of service provision that contributes to the smart city paradigm. The methodology integrates Intuitionistic Preference Relations (IPRs) with the Best-Worst Method (BWM) within the ambit of Multi-Criteria Decision Making (MCDM). IPRs play a pivotal role in this framework, providing a refined mechanism for capturing a spectrum of preferences, encompassing both affirmative and negational aspects through positive membership and non-membership functions. This aspect is particularly pertinent in group decision-making scenarios, where it facilitates aggregating diverse preference data by applying intuitive fuzzy arithmetic and weighted averaging operators. Additionally, the proposed methodology incorporates scoring and accuracy functions to prioritize and delineate the most feasible alternatives, adeptly navigating the intricate dynamics characteristic of urban settings. The BWM further augments this approach by providing a structured yet efficient, means of evaluating alternatives against multiple criteria. It begins with identifying the most and the least preferred criteria, followed by pairwise comparisons to establish their relative significance. It was applied in a hypothetical numerical scenario to appraise the efficacy of this proposed method. A comparative analysis was conducted utilizing the Analytic Hierarchy Process (AHP), another established pairwise comparison-based method. This comparison’s results underscore the proposed method’s superiority over AHP in several key metrics, including consistency ratio, minimum violation, total deviation, and overall conformity. In summary, the approach presented in this paper not only confronts the complexities inherent in urban environments but also lays the groundwork for more comprehensive and informed decision-making processes, thereby facilitating the evolutionary journey towards realising smart cities.

Keywords

• Best-worst method
• big data
• intuitionistic fuzzy sets
• QoE
• QoS
• smart city