Saraswati, Alya Resti (2025) Optimasi Rute Unmanned Aerial Vehicles Dalam Mencegah Penyebaran Kebakaran Semak Di Wilayah Metropolitan Perth Menggunakan Algoritma Population-Based Simulated Annealing. Other thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Dalam upaya mencegah penyebaran kebakaran semak, perencanaan rute pemantauan menggunakan sistem tak berawak seperti Unmanned Aerial Vehicle (UAV) menjadi langkah penting. Oleh karena itu, diperlukan perancangan rute UAV yang optimal guna mendukung pemantauan yang lebih efisien dan efektif. Optimasi rute pemantauan dirancang menggunakan pendekatan Multi-Depot Vehicle Routing Problem (MDVRP). Dalam tugas akhir ini, rute pemantauan Unmanned Aerial Vehicles (UAVs) dioptimalkan menggunakan algoritma Population-Based Simulated Annealing (PSA), sebuah metode metaheuristik yang memadukan kekuatan algoritma berbasis populasi dengan prinsip pendinginan bertahap. Penentuan lokasi depot dilakukan melalui klasterisasi data persebaran kebakaran semak di wilayah metropolitan Perth selama dua hari dengan tren peningkatan frekuensi kebakaran semak. Hasil klasterisasi tersebut kemudian digunakan sebagai dasar penentuan depot untuk merumuskan model matematis MDVRP, yang dioptimalkan menggunakan algoritma PSA. Pada tahap uji coba dan analisis terhadap tiga skenario data, PSA terbukti mampu menghasilkan rute yang mendekati optimal dan memiliki kinerja lebih baik dibandingkan algoritma Discrete Firefly Algorithm (DFA), dengan rata-rata jarak tempuh 0,16% lebih rendah dan waktu tempuh 0,20% lebih cepat. Hasil yang diperoleh dari tugas akhir ini diharapkan dapat digunakan untuk mendukung pengambilan keputusan dalam pencegahan penyebaran kebakaran semak di wilayah metropolitan Perth.
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To prevent the spread of bushfires, planning monitoring routes using unmanned systems such as Unmanned Aerial Vehicles (UAVs) becomes a crucial step. Therefore, optimizing UAV flight routes is essential to support effective and efficient monitoring, especially in the metropolitan Perth area, which is highly vulnerable to bushfires. The route optimization in this study is designed using the Multi-Depot Vehicle Routing Problem (MDVRP) approach. In this thesis, UAV monitoring routes are optimized using the Population-Based Simulated Annealing (PSA) algorithm, a metaheuristic method that combines the strengths of population-based algorithms with the principle of gradual cooling. Depot locations are determined through clustering of bushfire distribution data in the metropolitan Perth area over two consecutive days, which showed an increasing trend in bushfire frequency. The clustering results are then used as the basis for depot determination and formulation of the mathematical MDVRP model, which is subsequently optimized using the PSA algorithm. In the testing and analysis phase of the three data scenarios, PSA proved capable of generating near-optimal routes and outperformed the Discrete Firefly Algorithm (DFA), with an average travel distance that was 0.16% shorter and a travel time that was 0.20% faster. The results obtained from this final project are expected to support decision-making in preventing the spread of bushfires in the Perth metropolitan area.
| Item Type: | Thesis (Other) |
|---|---|
| Uncontrolled Keywords: | Kebakaran Semak, Multi-Depot Vehicle Routing Problem, Simulated Annealing, Unmanned Aerial Vehicle, Bushfire, Multi-Depot Vehicle Routing Problem, Population-Based Simulated Annealing, Unmanned Aerial Vehicle. |
| Subjects: | T Technology > T Technology (General) > T57.84 Heuristic algorithms. |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Information System > 57201-(S1) Undergraduate Thesis |
| Depositing User: | Alya Resti Saraswati |
| Date Deposited: | 28 Jul 2025 07:53 |
| Last Modified: | 28 Jul 2025 07:53 |
| URI: | http://repository.its.ac.id/id/eprint/122608 |
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