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Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem
Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is intro...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4488077/ https://www.ncbi.nlm.nih.gov/pubmed/26167171 http://dx.doi.org/10.1155/2015/375163 |
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author | Yue, Yi-xiang Zhang, Tong Yue, Qun-xing |
author_facet | Yue, Yi-xiang Zhang, Tong Yue, Qun-xing |
author_sort | Yue, Yi-xiang |
collection | PubMed |
description | Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA. |
format | Online Article Text |
id | pubmed-4488077 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-44880772015-07-12 Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem Yue, Yi-xiang Zhang, Tong Yue, Qun-xing Comput Intell Neurosci Research Article Vehicle Routing Problem (VRP) is one of the key issues in optimization of modern logistics system. In this paper, a modified VRP model with hard time window is established and a Hybrid Optimization Algorithm (HOA) based on Fractal Space Filling Curves (SFC) method and Genetic Algorithm (GA) is introduced. By incorporating the proposed algorithm, SFC method can find an initial and feasible solution very fast; GA is used to improve the initial solution. Thereafter, experimental software was developed and a large number of experimental computations from Solomon's benchmark have been studied. The experimental results demonstrate the feasibility and effectiveness of the HOA. Hindawi Publishing Corporation 2015 2015-06-16 /pmc/articles/PMC4488077/ /pubmed/26167171 http://dx.doi.org/10.1155/2015/375163 Text en Copyright © 2015 Yi-xiang Yue et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Yue, Yi-xiang Zhang, Tong Yue, Qun-xing Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem |
title | Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem |
title_full | Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem |
title_fullStr | Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem |
title_full_unstemmed | Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem |
title_short | Improved Fractal Space Filling Curves Hybrid Optimization Algorithm for Vehicle Routing Problem |
title_sort | improved fractal space filling curves hybrid optimization algorithm for vehicle routing problem |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4488077/ https://www.ncbi.nlm.nih.gov/pubmed/26167171 http://dx.doi.org/10.1155/2015/375163 |
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