%T PENYELESAIAN BI OBJECTIVE CAPACITATED VEHICLE ROUTING PROBLEM PADA PENGANTARAN GAS MENGGUNAKAN NON-DOMINATED SORTING GENETIC ALGORITHM II (NSGA-II)
%K Capacitated Vehicle Routing Problem, NSGA-II, Optimasi Multi-Objektif, Pareto Front, Distribusi Gas.

Capacitated Vehicle Routing Problem, NSGA-II, Multi-objective Optimization, Pareto Front, Gas Distribution.
%D 2025
%X Penelitian ini mengatasi masalah optimasi rute pada distribusi gas elpiji di sebuah pangkalan di Kabupaten Sleman, Yogyakarta, yang selama ini mengandalkan metode manual. Masalah ini diformulasikan sebagai Bi-Objective Capacitated Vehicle Routing Problem (BOCVRP) dengan dua tujuan: meminimalkan total jarak tempuh untuk menekan biaya operasional dan meminimalkan ketidakseimbangan muatan (load imbalance) antar kendaraan untuk meningkatkan efisiensi dan keselamatan. Untuk menyelesaikan masalah optimasi multi-objektif ini, diterapkan Non-dominated Sorting Genetic Algorithm II (NSGA-II). Algoritma ini diimplementasikan untuk menghasilkan sekumpulan solusi optimal yang dikenal sebagai Pareto Front. Proses ini melibatkan representasi rute sebagai kromosom, evaluasi fitness berdasarkan jarak dan keseimbangan muatan, serta mekanisme seleksi elitist menggunakan non-dominated sorting dan crowding distance. Hasil implementasi pada 31 pelanggan, 1 depot dan 2 kendaraan dengan kapasitas maksimal 60 buah tabung gas menunjukkan bahwa algoritma berhasil melakukan konvergensi dan menghasilkan serangkaian solusi non-dominan. Salah satu solusi optimal yang direkomendasikan mencapai total jarak tempuh 32.59 km dengan nilai ketidakseimbangan muatan sebesar 3.
Kata Kunci: Capacitated Vehicle Routing Problem, NSGA-II, Optimasi Multi-Objektif, Pareto Front, Distribusi Gas.

This research addresses the route optimization problem for LPG gas distribution at a depot in Sleman Regency, Yogyakarta, which has traditionally relied on manual methods. The problem is formulated as a Bi-Objective Capacitated Vehicle Routing Problem (BOCVRP) with two conflicting objectives: minimizing the total travel distance to reduce operational costs and minimizing the load imbalance among vehicles to enhance efficiency and safety. To solve this multi-objective optimization problem, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied. The algorithm is implemented to generate a set of optimal solutions known as the Pareto Front. This process involves representing routes as chromosomes, evaluating fitness based on distance and load balance, and employing an elitist selection mechanism using non-dominated sorting and crowding distance. The implementation, based on a case of 31 customers, 1 depot, and 2 vehicles with a maximum capacity of 60 gas each, demonstrates that the algorithm successfully converges and produces a set of non-dominated solutions. One of the recommended optimal solutions achieves a total travel distance of 32.59 km with a load imbalance value of 3.
%L repoupi139518
%O https://scholar.google.com/citations?hl=id&user=YZVlRKsAAAAJ

ID SINTA Dosen Pembimbing:
Kartika Yulianti: 5979108
Encum Sumiaty: 6142475
%A - Ade Lia Nur Fitri
%A - Kartika Yulianti
%A - Encum Sumiaty
%I Universitas Pendidikan Indonesia