SAPUTRA, CHANDRA RENOVAL (2026) PENERAPAN ALGORITMA GENETIKA PADA OPEN-ROUTE TRAVELING SALESMAN PROBLEM (Open-TSP) UNTUK OPTIMASI RUTE MULTI-DROP SEARAH DISTRIBUSI BARANG. S1 thesis, Universitas Mercu Buana Jakarta.
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Abstract
The delivery order for goods distribution with multiple destination addresses in a single invoice at Digital Studio Indonesia has so far been determined manually based on staff experience, which potentially results in suboptimal routes and increases travel distance, delivery time, and operational costs. This research aims to apply the Genetic Algorithm to solve the Open-Route Traveling Salesman Problem (Open-TSP), a variant of the Travelling Salesman Problem in which the vehicle is not required to return to the starting point after visiting all delivery destinations. The Genetic Algorithm was built independently without using any ready-made library, consisting of population initialization, fitness calculation (fitness = 1/total_distance), Tournament Selection, Order Crossover (OX), Swap Mutation, and elitism. The distance data between locations was obtained from the Google Routes API through its Compute Route Matrix feature, which represents actual road distances, while the delivery address input was facilitated by the Google Maps Places API. The system was implemented into an operational website based on Django with a PostgreSQL database. Testing using the Black-Box Testing method showed that all 18 test scenarios on the route optimization module were declared valid. Descriptive analysis results on three invoice scenarios with 5, 10, and 15 destination addresses showed that the routes generated by the Genetic Algorithm were consistently shorter than the manual routes, with a distance efficiency percentage ranging from 31.13% to 53.60%. These results prove that the application of the Genetic Algorithm to the Open-Route TSP, supported by real distance data from the Google Routes API, is able to produce more optimal and unidirectional distribution route recommendations compared to manually determined visit sequences. Kata kunci: Genetic Algorithm, Open-Route TSP, Distribution Route Optimization, Google Routes API, Django Distribusi barang dengan banyak alamat tujuan dalam satu invoice pada Digital Studio Indonesia selama ini masih ditentukan urutan pengirimannya secara manual berdasarkan pengalaman staf, sehingga berpotensi menghasilkan rute yang kurang optimal dan meningkatkan jarak tempuh, waktu, serta biaya operasional. Penelitian ini bertujuan menerapkan Algoritma Genetika untuk menyelesaikan permasalahan Open-Route Traveling Salesman Problem (Open-TSP), yaitu varian Travelling Salesman Problem yang tidak mengharuskan kendaraan kembali ke titik keberangkatan setelah seluruh tujuan pengiriman selesai dikunjungi. Algoritma Genetika dibangun secara mandiri tanpa menggunakan library siap pakai, dengan tahapan inisialisasi populasi, perhitungan fitness (fitness = 1/total_distance), Tournament Selection, Order Crossover (OX), Swap Mutation, dan elitism. Data jarak antar lokasi diperoleh dari Google Routes API pada fitur Compute Route Matrix yang merepresentasikan jarak jalan aktual, sedangkan input alamat pengiriman difasilitasi oleh Google Maps Places API. Sistem diimplementasikan ke dalam website operasional berbasis Django dan basis data PostgreSQL. Pengujian menggunakan metode Black-Box Testing menunjukkan seluruh 18 skenario pengujian pada modul optimasi rute dinyatakan valid. Hasil analisis deskriptif pada tiga skenario invoice dengan 5, 10, dan 15 alamat tujuan menunjukkan bahwa rute hasil Algoritma Genetika secara konsisten lebih pendek dibandingkan rute manual, dengan persentase efisiensi jarak berkisar antara 31,13% hingga 53,60%. Hasil ini membuktikan bahwa penerapan Algoritma Genetika pada Open-Route TSP dengan dukungan data jarak riil dari Google Routes API mampu menghasilkan rekomendasi rute distribusi yang lebih optimal dan searah dibandingkan penentuan urutan kunjungan secara manual. Kata kunci: Algoritma Genetika, Open-Route TSP, Optimasi Rute Distribusi, Google Routes API, Django
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