Optimasi Capacitated Vehicle Routing Problem with Time Windows dengan Menggunakan Ant Colony Optimization

Abstract

In recent years, minimization of logistics and transportation costs has become essential for manufacturing companies to increase profits. One thing is done to reduce logistics and transportation costs by optimizing the route of taking or transporting components from each supplier. Route optimization to minimize total transportation costs is a problem that often finds in Vehicle Routing Problems (VRP). Problem Capacitated Vehicle Routing with Time Windows (CVRPTW) is one variant of VRP that considers the vehicle capacity and the service period of each vehicle. CVRPTW is a Non-Polynomial Hard (NP-Hard) problem that requires an efficient and effective algorithm in solving problems that occur in this automotive company. This study uses the Ant Colony Optimization (ACO) algorithm by testing using several parameters to solve the CVRPTW problem. The test results using the ACO algorithm obtained a faster route compared to the method applied by the company. Available at: http://e-jurnal.lppmunsera.org/index.php/JSMI/article/view/1469

Algoritma General Variable Neighborhood Search Pada Capacitated Vehicle Routing Problem With Time Windows Dan Implementasinya

“…Salah satunya adalah penyelesaian CVRPTW menggunakan algoritma hybrid large-neighborhood search (Liu & Jiang, 2019). Algoritma genetika (Abouhenidi, 2014), algoritma Ant Colony Optimization (ACO) (Soenandi & Marpaung, 2019), dan penerapan CVRPTW pada pengumpulan sumbangan makanan (Guillermo et al, 2017).…”

A Decision Support Tool for Urban Freight Transport Planning Based on a Multi-Objective Evolutionary Algorithm

“…The meta-heuristics that have been applied in the UFT context are ant colony optimization [30], artificial bee colony [34], biogeography-based algorithms [23], the firefly algorithm [19], evolutionary algorithms [15], [17], [18], [24], [25], [31], [21], taboo search [33], [38] and simulated annealing [29], [40], among others. From the articles that address multi-objective versions of VRP with more than 20 nodes (clients), in more than 80% of them use, as said, meta-heuristics, and among them, in particular Evolutionary Multi-Objective Algorithms (MOEAs) [36].…”