| dc.contributor.advisor | Mayyani, Hidayatul | |
| dc.contributor.advisor | Bakhtiar, Toni | |
| dc.contributor.author | Girsang, Harley Dearmanson | |
| dc.date.accessioned | 2026-08-03T22:59:46Z | |
| dc.date.available | 2026-08-03T22:59:46Z | |
| dc.date.issued | 2026 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/176940 | |
| dc.description.abstract | Adaptasi armada kendaraan listrik pada sektor logistik memunculkan masalah optimasi rute kompleks yaitu Multi-Depot Electric Vehicle Routing Problem with Time Windows (MDEVRPTW). Penelitian ini bertujuan merancang rute optimal untuk persoalan tersebut dengan mengusulkan metode Hybrid Genetic Algorithm (HGA) yang diintegrasikan dengan pencarian lokal 2-Opt, penyisipan SPKLU dinamis, serta mekanisme delayed departure. Algoritma diuji pada berbagai skenario operasional lalu dibandingkan dengan hasil metode eksak Mixed-Integer Linear Programming (MILP). Hasil pengujian menunjukkan bahwa HGA secara konsisten merancang rute distribusi yang sepenuhnya feasible dengan total biaya operasional yang sangat kompetitif. HGA dapat memberikan solusi dalam hitungan detik. Meskipun masih terdapat gap solusi HGA hingga 10% pada skenario yang lebih kompleks, metode HGA menawarkan efisiensi komputasi yang tinggi sehingga sangat aplikatif diimplementasikan pada sistem distribusi logistik skala industri. | |
| dc.description.abstract | The adoption of electric vehicles in the logistics sector introduce a complex route optimization problem known as Multi-Depot Electric Vehicle Routing Problem with Time Windows (MDEVRPTW). This research aim to design optimal routes for this problem by proposing a Hybrid Genetic Algorithm (HGA) method integrated with a 2-Opt local search, dynamic charging station insertion, and a delayed departure mechanism. The algorithm performance is tested across various operational scenarios and compared with the exact Mixed-Integer Linear Programming (MILP) method. Experimental indicate that HGA consistently generate fully feasible distribution routes with competitive total operational costs and computation times in mere seconds. Although there is still a solution gap of up to 10% in more complex scenarios, this HGA method offers high computational efficiency, making it highly applicable for implementation in industrial-scale logistics distribution systems. | |
| dc.description.sponsorship | | |
| dc.language.iso | id | |
| dc.publisher | IPB University | id |
| dc.title | Optimasi Rute Kendaraan Listrik Multi-Depot dengan Time Windows Menggunakan Hybrid Genetic Algorithm | id |
| dc.title.alternative | | |
| dc.type | Skripsi | |
| dc.subject.keyword | Algoritma Genetika | id |
| dc.subject.keyword | Hybrid Genetic Algorithm | id |
| dc.subject.keyword | kendaraan listrik | id |
| dc.subject.keyword | multi depot | id |
| dc.subject.keyword | time windows | id |
| dc.subtype | Undergraduate Theses | |
