// <![CDATA[PENERAPAN ALGORITMA CLARKE AND WRIGHT SAVING DALAM CAPACITATED VEHICLE ROUTING PROBLEM DENGAN OPTIMASI NEAREST NEIGHBOR UNTUK RUTE TERPENDEK]]> 0415068801 - Yusup Miftahuddin, S.Kom., MT Dosen Pembimbing 1 Farrel Adi Ibrahim / 152020135 Penulis
Distribusi barang sering menghadapi permasalahan penentuan jalur yang sesuai dengan kapasitas kendaraan sekaligus meminimalkan jarak tempuh. Penelitian ini membahas penerapan algoritma Clarke and Wright Saving untuk membentuk rute awal distribusi ayam beku dengan kapasitas kendaraan tertentu, kemudian disempurnakan menggunakan algoritma Nearest Neighbor guna menyusun ulang urutan kunjungan pada setiap rute. Data jarak antar titik diperoleh dari distance matrix Google Maps API, sedangkan parameter distribusi mencakup kapasitas kendaraan, kecepatan rata-rata, dan waktu pelayanan di lokasi. Hasil penelitian menunjukkan bahwa penerapan Nearest Neighbor pada rute awal Clarke and Wright Saving menghasilkan jarak tempuh yang lebih pendek, dengan pengurangan 21.04% pada Rute 1, 20.03% pada Rute 2, dan 6.95% pada Rute 3. Jika dibandingkan dengan rute perusahaan, algoritma menghasilkan perbedaan pada setiap rute: Rute 1 dan Rute 2 lebih panjang dibanding perusahaan, sedangkan Rute 3 jauh lebih pendek dengan selisih 51.52 km (68.88%). Secara keseluruhan, kombinasi Clarke and Wright Saving dan Nearest Neighbor menempuh jarak 151.69 km dibandingkan 170.66 km pada rute perusahaan, sehingga terdapat pengurangan total 18.97 km atau 11.12%, dengan jumlah permintaan tetap terpenuhi sebanyak 300 ekor ayam beku. Meskipun hasil perhitungan menunjukkan adanya pengurangan jarak tempuh, penelitian ini masih bergantung pada data jarak tetap antar titik dan belum mempertimbangkan faktor dinamis seperti kondisi lalu lintas atau kendala operasional di lapangan. Oleh karena itu, penelitian selanjutnya dapat diarahkan pada pemanfaatan data waktu tempuh dinamis dan kondisi jalan aktual agar hasil perhitungan rute lebih sesuai dengan situasi distribusi nyata yang dihadapi perusahaan. Goods distribution often faces the problem of determining routes that match vehicle capacity while minimizing travel distance. This study discusses the application of the Clarke and Wright Saving algorithm to form the initial route for frozen chicken distribution with a specific vehicle capacity, which is then refined using the Nearest Neighbor algorithm to rearrange the order of visits on each route. The distance data between points is obtained from the Google Maps API distance matrix, while the distribution parameters include vehicle capacity, average speed, and service time at each location. The results show that applying the Nearest Neighbor algorithm to the initial Clarke and Wright Saving routes resulted in shorter travel distances, with reductions of 21.04% on Route 1, 20.03% on Route 2, and 6.95% on Route 3. When compared to the company's routes, the algorithm produced differences in each route: Routes 1 and 2 were longer than the company's routes, while Route 3 was much shorter with a difference of 51.52 km (68.88%). Overall, the combination of Clarke and Wright Saving and Nearest Neighbor covered a distance of 151.69 km compared to 170.66 km on the company's route, resulting in a total reduction of 18.97 km or 11.12%, with the demand for 300 frozen chickens still being met. Although the calculations show a reduction in distance traveled, this study still relies on fixed distance data between points and does not consider dynamic factors such as traffic conditions or operational constraints in the field. Therefore, further research can be directed towards utilizing dynamic travel time data and actual road conditions so that the route calculation results are more in line with the actual distribution situation faced by the company.