Application of Hexagonal Fuzzy MCDM Methodology for Site Selection of Electric Vehicle Charging Station

Abstract

In this paper, the application of hexagonal fuzzy multiple-criteria decision-making (MCDM) methodology for the site selection of electric vehicle charging stations is considered. In this regard, four factors and thirteen sub-factors have been taken into consideration for E-vehicle charging site selection. In this research, the geographic information system (GIS) has been incorporated with MCDM techniques. The fuzzy analytic hierarchy process (FAHP) is used to obtain a fuzzy weight of factors and sub-factors. MCDM tools fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) and fuzzy complex proportional assessment (FCOPRAS) have been used to rank the selected sites. A centroid-based method for defuzzification and distance measure between two hexagonal fuzzy numbers (HFN) has been developed for this paper. A practical example in Howrah, India, is considered to show the applicability and usefulness of the model. The results depict the suitability of the proposed research. Comparative and sensitivity analyses have been demonstrated to check the reliability, robustness and effectiveness of the proposed method.

Multi-Layer Attack Graph Analysis in the 5G Edge Network Using a Dynamic Hexagonal Fuzzy Method

“…Consider two HFNs M ~ = ( m 1 , m 2 , m 3 , m 4 , m 5 , m 6 ) and N˜ = ( n 1 , n 2 , n 3 , n 4 , n 5 , n 6 ), then the operation on these two HFNs are as follows [ 50 ]: Addition: M˜ ⊕ N˜ = ( m 1 + n 1 , m 2 + n 2 , m 3 + n 3 , m 4 + n 4 , m 5 + n 5 , m 6 + n 6 ); Subtraction: M˜ − N˜ = ( m 1 − n 6 , m 2 − n 5 , m 3 − n 4 , m 4 − n 3 , m 5 − n 2 , m 6 − n 1 ); Multiplication: M˜ × N˜ = ( m 1 × n 1 , m 2 × n 2 , m 3 × n 3 , m 4 × n 4 , m 5 × n 5 , m 6 × n 6 ); Division: M˜/N˜ = ( m 1 / n 6 , m 2 / n 5 , m 3 / n 4 , m 4 / n 3 , m 5 / n 2 , m 6 / n 1 ). (b) The Distance between Two HFNs …”

Uptake and Dissemination of Multi-Criteria Decision Support Methods in Civil Engineering—Lessons from the Literature

“…Thus, networks of electric vehicle charging stations may influence this choice and deciding on their locations is important. This issue was addressed in the city of Howra using fuzzy number formulations of AHP, TOPSIS and COPRAS using a total of thirteen criteria divided into four main groups (economic, environmental, traffic, and social) [132] and a sensitivity analysis in which the top ranking alternatives did not change, but the order of the lower ranking alternatives did change. The main papers are summarized in Table 11.…”

“…Distinguished between preferences of transport provider and regional and local government stakeholders. (various cities in Belgium) [132] AHP, TOPSIS and COPRAS 13 Choice of location for electric vehicle charging stations in Howrah (India) with sensitivity analysis.…”

Fuzzy-AHP MOORA approach for vendor selection applications

“…They use AHP because it provides a framework to make effective decisions in complex decision-making situations (e.g., vendor selection). However, in some weighting criteria using existing methods (such as the 1-9 scale pairwise comparisons), the criteria weighting cannot be measured only by a crisp value due to a lack of information, uncertainty, and ambiguity in human qualitative judgments [10]. Therefore, the decisionmakers hesitancy must be expressed in fuzzy sets [11], where fuzzy is likely more in line with the actual situations and can obtain more convincing ranking results [12].…”