Open Access

ARTICLE

A TSP-based nested clustering approach to solve multi-depot heterogeneous fleet routing problem

Mohammad Hassan Shojaeefard¹, Morteza Mollajafari², Seyed Hamid Reza Mousavitabar³, Mohammad Worya Khordehbinan⁴, Hossein Hosseinalibeiki⁵

1 Professor, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
2 Assistant professor, School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran
3 M.SC student, School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran
4 Department of Civil Engineering, KN Toosi University of Technology, Tehran, Iran
5 Department of Computer Engineering, Islamic Azad University, Safadasht Branch, Tehran, Iran

* Corresponding Author: Morteza Mollajafari ()

Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería 2022, 38(1), 1-11. https://doi.org/10.23967/j.rimni.2022.03.001

Accepted 15 February 2022; Issue published 08 March 2022

Abstract

The distribution of goods and urban services has made the issue of vehicle routing of particular importance to researchers. Advanced Routing Vehicle (RVRP) Rich Vehicle Routing Problem As a hybrid optimization problem, it is widely used in many transportation and logistics planning. The approach of this paper is to present a heuristic method for solving the problem called Nested Clustering for Traveling Salesman Problem (NC-TSP), in this method to optimize the search space, we break the problem in consecutive space. In the first step, using the nearest neighbor (Knn) algorithm with the center of each depot, and then using the fuzzy C-means clustering method within each cluster obtained from the Knn method, to find the optimal set of nodes. Then we solve the problem using the extension of MILP linear functions to the heterogeneous nature of the transport fleet and the warehouses that supply the goods, using the optimization algorithm (GA). The proposed approach, despite its great complexity, solves the problem to a large extent and shows promising cost-effective results in the existing criteria.

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Keywords

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