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

A systematic review of geographic information systems based methods and criteria used for electric vehicle charging station site selection

Many studies have incorporated particular models with various methods and algorithms to resolve the site selection problem for electric vehicle charging stations (EVCS). This paper systematically reviews research that evaluates geographic information systems (GIS) based EVCS location techniques and the variables used for decision making. We classify and characterize those techniques and variables to determine important linkages within the literature. A variety of databases were referenced to extract research published from 2010 to March 2023 pertinent to this specific location optimization problem, and 74 papers were selected after thorough evaluation. The models used in each paper were examined along with the methods for selecting variables and ranking alternate locations. Site selection for EVCS requires a multi-criteria decision making approach to meet the sustainability, efficiency, and performance goals of communities adopting electric vehicle mobility. Our results indicate that map algebra and data overlay methods have been used more frequently with GIS-based analysis than other techniques, while geographic and demographic variables are commonly the most significant site selection characteristics. The reviewed methods have most often been applied to urban locations; however, the transfer of these techniques to a rural EVCS site selection problem has been rarely explored in the current literature. This research assessment contributes relevant guidance for the application of methodologies useful in policymaking and provides recommendations for future research based on these findings.

Solving the Location Selection Problem of Self-Service Stores from the Perspectives of Sustainability and Uncertainty

This paper presents an improved method for selecting a specific location in the development of convenience stores in municipal areas. This method solves the problem of self-service store location from the perspective of sustainability and uncertainty and adequately considers the characteristics of individual locations with the proposition of an improved grey wolf optimization algorithm. The example presented shows that the improved algorithm has obvious advantages in facilitating the selection of convenience stores with respect to the search precision, stability, and convergence. Based on the macroenvironment, income, and cost, this work establishes a relatively complex, complete, and targeted mathematical model. Finally, taking the Xiaonanzhuang area of Suzhou Street in Beijing as an example, the scientificity, feasibility, and sustainability of the location model are verified.

An Intelligent Site Selection Model for Hydrogen Refueling Stations Based on Fuzzy Comprehensive Evaluation and Artificial Neural Network—A Case Study of Shanghai

With the gradual popularization of hydrogen fuel cell vehicles (HFCVs), the construction and planning of hydrogen refueling stations (HRSs) are increasingly important. Taking operational HRSs in China’s coastal and major cities as examples, we consider the main factors affecting the site selection of HRSs in China from the three aspects of economy, technology and society to establish a site selection evaluation system for hydrogen refueling stations and determine the weight of each index through the analytic hierarchy process (AHP). Then, combined with fuzzy comprehensive evaluation (FCE) method and artificial neural network model (ANN), FCE method is used to evaluate HRS in operation in China’s coastal areas and major cities, and we used the resulting data obtained from the comprehensive evaluation as the training data to train the neural network. So, an intelligent site selection model for HRSs based on fuzzy comprehensive evaluation and artificial neural network model (FCE-ANN) is proposed. The planned HRSs in Shanghai are evaluated, and an optimal site selection of the HRS is obtained. The results show that the optimal HRSs site selected by the FCE-ANN model is consistent with the site selection obtained by the FCE method, and the accuracy of the FCE-ANN model is verified. The findings of this study may provide some guidelines for policy makers in planning the hydrogen refueling stations.

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

Overall, 5G networks are expected to become the backbone of many critical IT applications. With 5G, new tech advancements and innovation are expected; 5G currently operates on software-defined networking. This enables 5G to implement network slicing to meet the unique requirements of every application. As a result, 5G is more flexible and scalable than 4G LTE and previous generations. To avoid the growing risks of hacking, 5G cybersecurity needs some significant improvements. Some security concerns involve the network itself, while others focus on the devices connected to 5G. Both aspects present a risk to consumers, governments, and businesses alike. There is currently no real-time vulnerability assessment framework that specifically addresses 5G Edge networks, with regard to their real-time scalability and dynamic nature. This paper studies the vulnerability assessment in the 5G networks and develops an optimized dynamic method that integrates the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the hexagonal fuzzy numbers to accurately analyze the vulnerabilities in 5G networks. The proposed method considers both the vulnerability and 5G network dynamic factors such as latency and accessibility to find the potential attack graph paths where the attack might propagate in the network and quantifies the attack cost and security level of the network. We test and validate the proposed method using our 5G testbed and we compare the optimized method to the classical TOPSIS and the known vulnerability scanner tool, Nessus.

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

The SCOPUS and Wed of Science bibliometric databases were searched for papers related to the use of multi-criteria methods in civil engineering related disciplines. The results were analyzed for information on the reported geographical distribution of usage, the methods used, the application areas with most usage and the software tools used. There was a wide geographical distribution of usage with all northern hemisphere continents well represented. However, of the very many methods available, a small number seemed to dominate usage, with the Analytic Hierarchy Process being the most frequently used. The application areas represented in the documents found was not widely spread and mainly seemed to be focused on issues such as sustainability, environment, risk, safety and to some extent project management, with less usage on other areas. This may be due to individual engineer’s choices in relation to if and how to disseminate the results of their work and to their choice of keywords and titles that determine if their publications are selected in bibliographic searches and thus more visible to a wider readership. A comparison with more topic focused searches, relating to Bridge Design, Earthquake Engineering, Cladding, Sewage Treatment, Foundation design, Truss design, Water Supply, Building Energy, Route selection and Transport mode showed very different results. Analysis of the papers in this area indicated that the full range of supporting software available for multi-criteria decision analysis (many listed in this paper) may not be fully appreciated by potential users.