Genetik Algoritma Tabanlı p-Medyan Yaklaşımı ile Elektrikli Araç Şarj İstasyonlarının Optimal Konumlandırılması: Düzce Örneği

Abstract

Elektrikli araç kullanımının hızla artmasıyla birlikte, şarj istasyonlarının optimum konumlandırılması hem erişilebilirlik hem de maliyet etkinliği açısından önemli hale gelmiştir. Bu çalışma, Düzce ili için en çok talep gerektirecek noktalara elektrikli araç şarj istasyonlarının optimum yerleşimini belirlemek amacıyla P-medyan tesis yeri seçimi kullanan genetik algoritma (GA) tabanlı bir model önermektedir. Çalışmada Düzce merkez ve çevresine ait 44 aday istasyon noktası ve 5000 talep noktasından oluşan kapsamlı bir mekânsal veri seti kullanılmaktadır. Şarj istasyonlarının optimal konumlandırılması amacıyla GA tabanlı Rulet Tekerleği, Turnuva, Rassal Çözüm gibi farklı seçim ve farklı mutasyon operatörleri kullanılarak problemin çözümü için en uygun operatör tiplerinin de belirlenmesi sağlanmıştır. Elde edilen sonuçlar göre, özellikle Turnuva seçiminin hem yakınsama hızı hem de elde edilen nihai maliyet değerleri bakımından diğer GA operatörlerine göre üstünlük sağladığı görülmektedir. Ayrıca, konumsal atama analizleri, modelin kent merkezindeki talep yoğunluklarını doğru biçimde yansıttığını ve optimal çözümlerin mekânsal kümelenme eğilimleri ile uyumlu olduğu değerlendirilmiştir. Bu yönleriyle çalışma, elektrikli araç şarj altyapısının en uygun maliyet ve mesafe açısından konumlandırılması alanında hem yöntemsel hem de uygulamalı düzeyde katkı sağlamakta ve meta-sezgisel yöntemlerin parametre duyarlılığını gerçek veri üzerinde ortaya koyan özgün bir değerlendirme sunmaktadır. Sonuç olarak, elde edinilen sonuçlar kentsel veya genel ölçekte şarj altyapısı planlamasında GA yaklaşımının etkinliğini doğrulamakta ve parametre duyarlılığına ilişkin özgün bir değerlendirme sunarak literatüre katkı sağlamaktadır.

Keywords

• Elektrikli araçlar
• Şarj istasyonu konumu
• Genetik algoritma
• P-medyan tesis konumu seçim problemi

Optimal Placement of Electric Vehicle Charging Stations in Düzce Province Using Binary Particle Swarm Optimization and Genetic Algorithm

Abstract

This study aims to determine the most suitable locations for electric vehicle charging stations within the borders of Düzce province. A p-median-based Genetic Algorithm (GA) method was used in the site selection process. As an alternative solution approach, the Binary Particle Swarm Optimization (BPSO) algorithm was utilized. Detailed spatial data covering the Düzce city center and its surroundings were used in the study; 44 potential station points were identified, and 5,000 demand points to be directed to these points were defined. Different selection and mutation operators were tested within the GA method to determine the most suitable charging station locations. Operators such as Random Solution, Tournament Selection, and Roulette Wheel were compared. The study specifically examined which method provided the most efficient distribution for a region like Düzce. According to the results obtained, the Tournament Selection method yielded more successful results in terms of both cost and performance compared to other operators. Spatial analyses show that the model accurately reflects areas with high demand. Furthermore, it was observed that the most efficient solutions are clustered in specific areas. Another aim of the study is to comparatively evaluate the results obtained from the GA and BPSO methods. The findings revealed that BPSO offers faster and higher-quality solutions, especially in binary positioning problems. In this respect, BPSO stands out as a strong and feasible option for charging station planning. In conclusion, this study makes significant contributions to literature, both methodologically and practically. In analyses conducted with real field data, the GA and BPSO algorithms were compared via the p-median model; valuable information was obtained regarding the performance of these heuristic methods in complex urban structures.

Keywords

• Electric vehicles
• Charging stations location
• Genetic algorithm
• P-median selection problem
• Binary particle swarm optimization

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