Tujuan dari makalah ini adalah untuk merancang sebuah solusi dari permasalahan rute kendaraan dalam mendistribusikan bahan dengan menggunakan perbandingan 4 jenis algoritma metaheuristik yaitu: Algoritma Genetika (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) dan Cross Entropy (CE) dengan beberapa kombinasi parameter yang digunakan untuk menjalankan algoritma. Kami menggunakan studi kasus masalah routing dari perusahaan distribusi dalam mendistribusikan bahan baku pada outlet- outletnya, yang memiliki 10 node (outlet),dengan menggunakan data dari posisi node dan tingkat lintasan (waktu kedatangan pada node).Hasil dari 4 (empat) algoritma ditemukan bahwa GA, PSO dan ACO memiliki nilai optimasi yang lebih baik daripada iterasi CE dan membutuhkan lebih banyak sumber daya untuk waktu komputasi. Dalam kesimpulan akhir diperoleh waktu komputasi paling cepat adalah CE, sedangkan waktu komputasi paling lambat adalah GA, waktu yang memungkinkan untuk distribusi per hari ± 6 jam ditetapkan jumlah kendaraan yang dibutuhkan sebanyak 3 unit, dengan total jarak 60 km dan total waktu 6 jam (kecepatam rata-rata 10km/jam). Kata kunci:Vehicle Routing Problem, Metaheuristic, Optimasi

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

Creating effective and efficient production planning becomes one of the most intriguing efforts for most of the Indonesian enterprises. This study introduces an optimization model of product mix to solve the production planning problems, by considering certain multi-constraint: limited existing resources, objectives to be achieved, and fuzzy characteristics of demand and production costs. By applying Fuzzy Mixed Integer Linear Goal Programming, this study tries to determine the optimal solutions of product mix. The study consists of several steps: capacity constraint resource analysis, formulation of the optimization models, determines the products mix and the multi-criteria objective value. The proposed product mix model is then validated by conducting a preliminary study to one enterprise. The preliminary study showed that the proposed model is able to provide an increase of multi-criteria objective value by 4.81% compared to the existing conditions.

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.

No abstract