Siting Of Electric Vehicle Charging Stations Research Articles

Multi-criteria decision analysis model using the q-rung orthopair fuzzy similarity measures and the COPRAS method for electric vehicle charging station site selection

Multi-criteria decision analysis model using the q-rung orthopair fuzzy similarity measures and the COPRAS method for electric vehicle charging station site selection

Multiple attribute group decision making based on quasirung orthopair fuzzy sets: Application to electric vehicle charging station site selection problem

This paper aims to greatly improve decision makers’ capability of capturing their judgment in a broader space. In order to achieve this, we introduce the notion of a p,q-quasirung orthopair fuzzy set (p,q-QOFS), which is an extension of the q-rung orthopair fuzzy set. In p,q-QOFS, the sum of the pth power of membership degree and qth power of nonmembership degree is less than or equal to 1, where p and q are natural numbers. Thus, due to the additional parameter p, the p,q-QOFS can express incomplete information more flexibly and elaborately. This paper first develops the concept of p,q-QOFS and establishes that it is an extension of several existing fuzzy sets. Then, we introduce the score and accuracy functions of p,q-QOFS and analyze a few mathematical properties. Next, we define the Hamming distance measure between two p,q-QOFSs and some important properties. After that, we investigate the basic operations of p,q-QOFSs and extend these operational laws to aggregation operators. Further, we introduce the weighted averaging and geometric aggregation operators to aggregate p,q-quasirung orthopair fuzzy data. Moreover, we establish a p,q-quasirung orthopair fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for solving multi-attribute group decision-making problems with unknown attribute weights. The present study also discusses a case study illustrating the applicability of the method through the selection of the most appropriate site for an electric vehicle charging station in an Indian city. In this case study, we consider seven alternative sites, including Raniganj, Jamuria, Kulti, and Burnpur. As a result of this study, Jamuria appears to be the best location to build an electric vehicle charging station. Finally, we illustrate the validity and practicability of our developed method through a comparative analysis with existing methods.

Conversion-based aggregation algorithms for linear ordinal rankings combined with granular computing

This paper proposes two aggregation algorithms for aggregating individuals’ linear ordinal rankings (LORs) in group decision-making environment, which improves the computability of LORs. At first, in order to represent the position information of alternatives, we depict LORs by extended preference map, and then analyze the basic statistical quantitative characteristics of the LORs. Subsequently, combining with the concept of granular computing, we derive the arithmetic expressions of interval utility values for alternatives, in which the information granularity indices are introduced to determine the interval lengths. Later on, two programming​ models are established with the aim of minimizing the differences between individual opinions and the corresponding collective one. Besides, in the two models, we propose the methods to determine experts’ weights according to the differences of experts’ knowledge backgrounds. Both information granularity index and the aggregated interval utility value can be calculated by solving the models, based on which the final aggregated ranking can be determined. Furthermore, a numerical case concerning the electric vehicle charging station site selection problem is presented to illustrate the usage of the proposed algorithms, and finally, the efficiency and features of the two algorithms are exhibited through comparative analyses and simulation experiment.

A Systematic Review of Geographic Information Systems Based Methods and Criteria Used for Electric Vehicle Charging Station Site Selection

A Systematic Review of Geographic Information Systems Based Methods and Criteria Used for Electric Vehicle Charging Station Site Selection

A Decision Framework for Electric Vehicle Charging Station Site Selection for Residential Communities under an Intuitionistic Fuzzy Environment: A Case of Beijing

Electric vehicle charging station (EVCS) site selection occupies a prominent position in the development of the electric community to solve the hard problem of Electric Vehicle (EV) charging. However, two critical issues have not been solved by the existing research. Firstly, the scope of EVCS site selection only considers the whole city, which deviates from the actual situation. Secondly, the uncertainty and hesitation of decision information is not well expressed. To handle the above problems, this paper builds a comprehensive EVCS site selection decision framework for Residential Communities (EVCSRC) with triangular intuitionistic fuzzy numbers (TIFNs). First of all, the distinctive index system of EVCSRC site selection factors including economy, social, environment, planning and feature portrait of residential communities is established. Then, the TIFNs is utilized for decision makers (DMs) to express the indeterminate information. Furthermore, a fuzzy Vlsekriterijumska Optimizacija I Kompromisno Resenje (Fuzzy-VIKOR) is approach is utilized to rank the alternative EVCSRC sites. Finally, a case of Beijing is studied to demonstrate the validity of the proposed site selection framework. The result shows the EVCSRC site located at Sijiqing community in Haidian district should be selected as the optimal site. This paper presents a feasible and easy-to-use decision-making framework for investors.