Please use this identifier to cite or link to this item: https://ruomoplus.lib.uom.gr/handle/8000/1705
Title: A variable neighborhood search approach for solving a real-world hierarchical multi-echelon vehicle routing problem involving HCT vehicles
Authors: Tadaros, Marduch 
Sifaleras, Angelo 
Migdalas, Athanasios 
Author Department Affiliations: Luleå University of Technology 
Department of Applied Informatics 
Luleå University of Technology 
Author School Affiliations: School of Information Sciences 
Keywords: Adaptive search
Variable neighborhood search
High capacity transports
Vehicle routing
Issue Date: 1-May-2024
Publisher: Elsevier
Journal: Computers & Operations Research 
ISSN: 0305-0548
Volume: 165
Start page: 106594
End page: 106608
Abstract: 
This paper studies the Hierarchical Multi-Switch Multi-Echelon VRP (HMSME-VRP), a newly introduced VRP variant based on a real-world case involving High Capacity Vehicles (HCV). The problem originates from the policies of a distribution company in the Nordic countries where HCVs of up to 34.5 m and up to 76 tons are allowed. The HMSME-VRP offer a new way to model distribution problems to cover large geographical areas without substantial costs in infrastructure. Furthermore, it adds complexity to the standard VRP and, as such, remains NP-hard and difficult to solve to optimality. Indeed, it has been demonstrated that only very small instances can be solved to optimality by a commercial solver. Thus, in order to handle instances of real-world size, we propose two General Variable Neighborhood Search (GVNS) procedures, the second of which is adaptive, utilizing an intelligent reordering mechanism. In order to evaluate the proposed procedures, 48 benchmark instances of various sizes and characteristics are generated and made publicly available, comprising of clustered, random, and semi-clustered customers. The computational results show that both GVNS procedures outperform the exact solver. Additionally, the adaptive version outperforms the conventional version based on both average and best solutions. Furthermore, we present a statistical analysis to verify the superiority of the adaptive version.
URI: https://ruomoplus.lib.uom.gr/handle/8000/1705
DOI: 10.1016/j.cor.2024.106594
Rights: Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές
Corresponding Item Departments: Luleå University of Technology
Department of Applied Informatics
Luleå University of Technology
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