Volume 36, Issue 3 (IJIEPR 2025)
IJIEPR 2025, 36(3): 169-185 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Islam M R, Rahman M A, Nazib K M, Ali L E. Clustering Based Intelligent Route Optimization Algorithm for Capacitated Vehicle Routing Problem. IJIEPR 2025; 36 (3) :169-185
URL: http://ijiepr.iust.ac.ir/article-1-2116-en.html
URL: http://ijiepr.iust.ac.ir/article-1-2116-en.html
1- Department of Mathematics, Hamdard University Bangladesh, Gazaria, Munshiganj 1510, Bangladesh.
2- Associate Professor, Mathematics Discipline, Khulna University, Khulna-9208, Bangladesh ,mdazizur@math.ku.ac.bd
3- Mathematics Discipline, Science, Engineering and Technology School, Khulna University, Khulna 9208, Bangladesh.
2- Associate Professor, Mathematics Discipline, Khulna University, Khulna-9208, Bangladesh ,
3- Mathematics Discipline, Science, Engineering and Technology School, Khulna University, Khulna 9208, Bangladesh.
Abstract: (336 Views)
The Capacitated Vehicle Routing Problem (CVRP) is a significant variant of the vehicle routing problem that incorporates constraints related to customer demand and vehicle capacity. Owing to its extensive applications in logistics and transportation, CVRP has attracted substantial research attention, with numerous algorithms proposed from the perspective of intelligent search. A common solution strategy involves two phases: first, assigning customers to different vehicles to form feasible routes, and second, optimizing these routes. This paper presents a two-phase CVRP solution framework through the clustering concept with intelligent search to improve route planning. In the first phase, a set of clustering methods - fuzzy c-means, k-means, and k-medoids - combined with a nearest neighbor heuristic search, are applied to generate feasible routes for each vehicle. In the second phase, these routes are iteratively optimized using the Simulated Annealing (SA) algorithm. The process yields three distinct solution pathways: fuzzy c-means with SA, k-means with SA, and k-medoids with SA. For performance evaluation, 46 benchmark CVRP datasets from a publicly available library are used. Simulation results demonstrate that k-means with SA performs the best, surpassing the other two approaches and outperforming other clustering-based two-phase state-of-the-art algorithms in terms of solution quality.
Keywords: Combinatorial Optimization, Capacitated Vehicle Routing Problem, Two-phase Algorithm, Clustering Algorithm, Simulated Annealing Algorithm.
Type of Study: Research |
Subject:
Logistic & Apply Chain
Received: 2024/09/15 | Accepted: 2025/09/8 | Published: 2025/09/26
Received: 2024/09/15 | Accepted: 2025/09/8 | Published: 2025/09/26
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |