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Performance benchmarking of or-tools methods for capacitated vehicle routing problems with time windows

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Detalhes bibliográficos
Resumo:The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a significant challenge in combinatorial optimization, with extensive practical applications in logistics and transportation. This study aims to conduct a comparative analysis of the various methods available in OR-Tools for solving the CVRPTW across datasets of different sizes and types using the Solomon and the Gehring and Homberger benchmarks. The analysis provided insights into the relative strengths of each method, with a primary focus on Guided Local Search (GLS) and Tabu Search (TS), showing consistent performance and adaptability to different dataset characteristics. The results indicate that GLS is the most robust method overall, and TS can outperform it in specific scenarios. In conclusion, this study offers insights for selecting the most effective method to solve vehicle routing problems based on the characteristics and scale of the problem.
Autores principais:Sena, Inês
Outros Autores:Ribeiro, Tiago B.; Silva, Adriano S.; Fernandes, Florbela P.; Costa, Lino A.; Pereira, Ana I.
Assunto:Capacitated Vehicle Routing Problem Optimization OR-Tools Time Windows
Ano:2026
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso restrito
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB
Descrição
Resumo:The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a significant challenge in combinatorial optimization, with extensive practical applications in logistics and transportation. This study aims to conduct a comparative analysis of the various methods available in OR-Tools for solving the CVRPTW across datasets of different sizes and types using the Solomon and the Gehring and Homberger benchmarks. The analysis provided insights into the relative strengths of each method, with a primary focus on Guided Local Search (GLS) and Tabu Search (TS), showing consistent performance and adaptability to different dataset characteristics. The results indicate that GLS is the most robust method overall, and TS can outperform it in specific scenarios. In conclusion, this study offers insights for selecting the most effective method to solve vehicle routing problems based on the characteristics and scale of the problem.