Genetic Algorithm Approach based on Graph Theory for Location Optimization of Electric Vehicle Charging Stations

Altundoğan, Turan Göktuğ; Yıldız, Ahmet; Karaköse, Ebru

doi:10.1109/asyu52992.2021.9598951

Cited by 15 publications

(2 citation statements)

References 14 publications

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“…According to the GA, in each new generation, individuals with high-quality and low-quality genes from the previous generation and the most appropriate distribution of traits survive, but incompetent individuals cannot. In this way, the tendency for good and strong traits to be seen in new generations is greater [27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

A New Last Mile Delivery Approach for the Hybrid Truck Multi-Drone Problem Using a Genetic Algorithm

Karaköse

2024

Applied Sciences

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In this study, the use of drones in transportation and some optimization studies carried out in the literature so far were examined. Based on these investigations, a genetic algorithm-based method has been proposed to solve the hybrid truck multi-drone problem. With the proposed algorithm, scenarios have been created using single or multiple drones and one truck for a certain number of customers to be delivered, and results have been obtained. In these scenarios, the main aim is to minimize delivery time. The results obtained have also been compared with other optimization algorithms and other results in the literature, and in addition, it has been proven that there is an inverse relationship between the number of drones and the delivery time. As a result of the comparisons, it has been clearly seen that the proposed algorithm is quite successful in finding the most suitable route in the shortest time compared to other studies. A solution has been created for a real-world problem with the proposed genetic algorithm-based algorithm, and it has been concluded that it is quite successful here as well.

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Section: Related Workmentioning

confidence: 99%

A New Last Mile Delivery Approach for the Hybrid Truck Multi-Drone Problem Using a Genetic Algorithm

Karaköse

2024

Applied Sciences

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“…In addition [34], an optimization model using a GA was developed to determine the number and location of EV charging stations, which aims to minimize costs and improve service quality by reducing travel distances between demand points and stations. Altundogan et al [40], a GA approach based on graph theory is used to determine the optimal locations for charging stations in urban areas, by considering the path relation and distance information between reference nodes in a weighted graph, resulting in a suitable method for distance-based charging station location determination. Srithapon et al [23] presents an optimized strategy for EV charging scheduling in an urban village, considering energy arbitrage and distribution network costs, using a GA, resulting in reduced energy arbitrage loss, peak demand, power loss, and transformer aging.…”

Section: Current State-of-the-art Electric Vehicle Charging Optimizat...mentioning

confidence: 99%

Recent trend and future prospect in optimization of electric vehicle charging: a systematic review

Fauzi,

Zakri,

Syafii

2024

Bulletin EEI

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Electric vehicles charging (EVs) must be done optimally to minimize the impact it causes. EVs are being recognized as a potential way to decrease greenhouse gas emissions and combat climate change. However, there are still difficulties in optimizing these systems to minimize operating costs and EVs charging waiting times. This study investigates several industrial, commercial and residential charging stations. The primary objective of this study is to systematically review the existing literature on optimizing EV charging. The collection of data was centered on scholarly articles released between the years 2018 and 2023 from Scopus, IEEE Xplore. This study presents a systematic literature review of optimizing EVs charging. As a result, 43 EVs charging optimization studies were obtained which were investigated and studied further. Identify and analysis the selected studies, there are two research topics and trends most frequently addressed by researchers: scheduling and coordination. The four most applied methods in EVs charging are identified: particle swarm optimization (PSO), genetic algorithm (GA), linear programming (LP) method, and evolutionary algorithms (EA). Future research directions: develop advanced optimization algorithms, investigating the integration of renewable energy sources into the charging infrastructure, exploring the potential of vehicle-to-grid (V2G) services, studying the impact of EVs charging on the power grid and developing strategies, considering the optimization of charging schedules and coordination strategies for large-scale EVs fleets.

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Optimal Location of Electric Vehicle Charging Stations in Distribution Grids Using Genetic Algorithms

Gomes,

Cerveira,

Baptista

2024

Communications in Computer and Information Science

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Genetic Algorithm Approach based on Graph Theory for Location Optimization of Electric Vehicle Charging Stations

Cited by 15 publications

References 14 publications

A New Last Mile Delivery Approach for the Hybrid Truck Multi-Drone Problem Using a Genetic Algorithm

A New Last Mile Delivery Approach for the Hybrid Truck Multi-Drone Problem Using a Genetic Algorithm

Recent trend and future prospect in optimization of electric vehicle charging: a systematic review

Optimal Location of Electric Vehicle Charging Stations in Distribution Grids Using Genetic Algorithms

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