Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Brilliance: Research of Artificial Intelligence
I Gusti Ayu Novitasari
Universitas Bali Dwipa, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Application of Ant Colony Optimization on CVRP for Waste Collection Route Optimization in Marga Village Ida Bagus Kade Puja Arimbawa K; I Gusti Ayu Novitasari; Putu Nanda Andika Permana
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.7006

Abstract

Marga Village, located in Marga District, Tabanan Regency, faces significant challenges in waste management due to the absence of a structured schedule and route for waste collection, leading to inefficiencies, high operational costs, infrastructure risks, and public health concerns. These issues are further exacerbated by population growth and spatial expansion, which continually increase waste volume. This study aims to optimize waste collection routes in Marga Village by applying the Capacitated Vehicle Routing Problem (CVRP) approach using the Ant Colony Optimization (ACO) algorithm to identify the most efficient and sustainable shortest route. The simulation considered two main constraints: a maximum vehicle capacity of 1.2 m³ and an average waste volume per point ranging from 0.04 to 0.2 m³, ensuring load feasibility. The model was tested with 10 ants over 10 iterations, with temporary disposal points located at a? (Banjar Lebah) and c? (Banjar Beng) before transportation to TPS3R. Algorithm parameters were set at ? = 1.0 for pheromone influence and ? = 5.0 for visibility, while the pheromone evaporation rate (?) was set to 0.5 and Q = 100 was used to reinforce optimal paths. The results demonstrate that ACO can effectively solve CVRP in waste collection, offering a data-driven solution to improve route efficiency and support sustainable urban waste management planning.
Co-Authors Ida Bagus Kade Puja Arimbawa K. Putu Nanda Andika Permana