Aplikasi Model Rute Kendaraan Listrik Dengan Kombinasi Algoritma Genetika Dan Tabu Search

Fauziah, Nur Farah (2026) Aplikasi Model Rute Kendaraan Listrik Dengan Kombinasi Algoritma Genetika Dan Tabu Search. Other thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Penelitian ini membahas permasalahan Electric Vehicle Routing Problem (EVRP) dengan mempertimbangkan keterbatasan kapasitas baterai kendaraan listrik serta keberadaan stasiun pengisian daya. Metode yang digunakan adalah Algoritma Genetika (GA) dan kombinasi Algoritma Genetika dengan Tabu Search (GA-TS) untuk memperoleh rute distribusi yang optimal. Pengujian dilakukan pada lima skenario dengan jumlah 28 lokasi pasar dan satu depot, menggunakan kapasitas baterai sebesar 200 km dengan ambang batas pengisian daya sebesar 20% dari kapasitas baterai. Hasil uji coba menunjukkan bahwa metode GA-TS menghasilkan performa yang lebih baik dibandingkan GA berdasarkan total jarak tempuh dan sisa baterai akhir. Pada metode GA, kendaraan listrik pada beberapa skenario mengalami kehabisan energi sehingga memerlukan proses pengisian daya untuk menyelesaikan seluruh rute. Sebaliknya, metode GA-TS mampu menyelesaikan seluruh kunjungan tanpa melakukan charging karena mampu menemukan rute terpendek sehingga penggunaan energi lebih efisien.
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This study addresses the Electric Vehicle Routing Problem (EVRP) by considering the limited battery capacity of electric vehicles and the availability of charging stations. The methods used are Genetic Algorithm (GA) and a combination of Genetic Algorithm with Tabu Search (GA-TS) to obtain optimal distribution routes. Testing was conducted on five scenarios with a total of 28 market locations and one depot, using a battery capacity of 200 km with a charging threshold of 20% of the battery capacity. The test results show that the GA-TS method produces better performance than GA based on the total distance traveled and the final battery remaining. In the GA method, electric vehicles in some scenarios run out of energy so they need a charging process to complete the entire route. In contrast, the GA-TS method is able to complete all visits without charging because it is able to find the shortest route so that energy use is more efficient.

Item Type:	Thesis (Other)
Uncontrolled Keywords:	Electric Vehicle Routing Problem, Algoritma Genetika, Tabu Search Electric Vehicle Routing Problem, Genetic Algorithm, Tabu Search
Subjects:	Q Science Q Science > QA Mathematics Q Science > QA Mathematics > QA166 Graph theory
Divisions:	Faculty of Science and Data Analytics (SCIENTICS) > Mathematics > 44201-(S1) Undergraduate Thesis
Depositing User:	Nur Farah Fauziah
Date Deposited:	29 Jan 2026 03:38
Last Modified:	29 Jan 2026 03:38
URI:	http://repository.its.ac.id/id/eprint/130926

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