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Enhancing Transportation Cost Efficiency through Capacitated Vehicle Routing Problem (CVRP) Implementation in Distribution Systems Siti Fatimah; Septi Riyani; Adinda Sukma Novelia; Danny Dwi Rachmanto; Feni Ira Puspita; Rizal Ardianto
JUMANTARA: Jurnal Manajemen dan Teknologi Rekayasa Vol 5, No 2 (2026)
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/jumantara.v5i2.3936

Abstract

This study analyzes and optimizes the distribution system of UD Arum Sari, a mackerel cracker producer in Mojokerto, serving 21 delivery points across East and Central Java through three main routes. Previous distribution activities relied on conventional routing decisions without mathematical optimization, resulting in inefficient travel distances and operational costs. Research applying the Capacitated Vehicle Routing Problem (CVRP) in small-scale food distribution systems with fixed and variable transportation cost structures remains limited. Therefore, this study aims to minimize total distribution costs and travel distances without adding vehicles or facilities. The study employs a quantitative discrete optimization approach using the CVRP model by considering vehicle capacity, customer demand, and transportation cost components. The CVRP model was solved using a spreadsheet-based Solver optimization module with binary routing variables, capacity constraints, and an objective function to minimize distance-related transportation costs. The resulting route is optimal within the formulated deterministic CVRP model and feasible under the 1,100 kg vehicle-capacity constraint. The results show that Route A reduced travel distance from 224 km to 171.2 km with cost efficiency of 14.95%. Route B decreased from 672.8 km to 563.5 km with savings of 12.74%, while Route C declined from 617 km to 565.8 km with 6.03% efficiency. These findings confirm that CVRP improves distribution efficiency and supports effective logistical decision-making through mathematical optimization.