Akademik Veri Yönetim Sistemi
Journal of the Faculty of Engineering and Architecture of Gazi University, cilt.40, sa.3, ss.1875-1886, 2025 (SCI-Expanded, Scopus, TRDizin)
Since the Industrial Revolution, at the end of the extensions to energy, there has been a series of largely air pollution in the environment. Countries keep increasing the containment of green alternative fuels, including electricity, so that the rapid growth of air pollution can be stopped/reduced. In this context, a new multi objective mixed integer linear programming model has been developed to determine when, where, and at what capacity the charging stations will be installed, in a way that would satisfy the demand of electric vehicles (EV) from the nearby areas and minimize the cost of electric vehicle charging station installation and operation. Demands are handled realistically to make an appropriate investment plan, considering various features, including vehicle brands and models. The overall performance of the proposed model is examined on randomly generated 15 different test problems randomly generated by computational experiments. For the solution of the problems, the Pareto Front (PC), which is obtained by using the multi-objective optimization methods Lexicographic Optimization Method and AUGMECON2, provides the decision maker with trade-off information for different levels of the objective functions. Table A shows the computational results obtained by Lexicographic Optimization method of small-size test problems. Table A. Computational results of small-size test problems P# f1idealf2nadir f2ideal Solution f1nadir Solution Time (sec.) Time (sec.) 15_1 342906,9 6228,658 4169,76 1748,175 664980,6 1,88 15_2 244927,2 5152,749 368,75 1001,557 595636,5 1,88 15_3 241307,5 5474,044 821,37 1035,589 604959,6 2,76 30_1 759439,3 6263,145 22699,72 1940,183 1362374 11,19 30_2 - - - 1717,916 1416644 7,28 30_3 - - - 1605,448 1387817 11,27 Purpose: The purpose of this study is to develop an investment plan that satisfy the demand of EV from the nearby areas and minimize the cost of electric vehicle charging station installation and operation. Theory and Methods: To dealing with many objectives and time periods in the context of modeling, a multi objective mathematical model has been proposed. The model was solved using lexicographic optimization and the AUGMECON2. The resulting PFs are also displayed on the graphs and provided to the decision maker trade-off information to help his/her choose an appropriate solution. Results: Range anxiety, one of the most common concerns among drivers, will be eliminated, and a significant step toward a cleaner environment will be taken. Conclusion: Studies in the literature that have addressed the electric vehicle charging station location problem up to now, it has never been considered the battery and power specifications among electric vehicles and charging stations. In this study, vehicle brands and models, battery and power capacities of the EV and the charger in the station have been considered to addressing the realistic demands of electric vehicles.