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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2018 - 7

A Hybrid Model based on Genetic Algorithm and Space-Filling Curve applied to Optimization of Vehicle Routes

MENDES, W. R. See more information about MENDES, W. R. on SCOPUS See more information about MENDES, W. R. on IEEExplore See more information about MENDES, W. R. on Web of Science, PEREIRA, F. G. See more information about  PEREIRA, F. G. on SCOPUS See more information about  PEREIRA, F. G. on SCOPUS See more information about PEREIRA, F. G. on Web of Science, CAVALIERI, D. C. See more information about CAVALIERI, D. C. on SCOPUS See more information about CAVALIERI, D. C. on SCOPUS See more information about CAVALIERI, D. C. on Web of Science
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Author keywords
vehicle routing, genetic algorithms, fractals, hybrid intelligent systems, computer applications

References keywords
problem(16), routing(14), vehicle(13), genetic(10), algorithm(9), research(6), windows(5), time(5), salesman(5), optimization(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2018-08-31
Volume 18, Issue 3, Year 2018, On page(s): 45 - 52
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.03007
Web of Science Accession Number: 000442420900007
SCOPUS ID: 85052128618

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This work is the result of a real problem in the Sanitation Company of Espirito Santo (Companhia Espirito Santense de Saneamento), which owns a Geographic Information System, but lacks a mechanism to build routes to server customers that open in average 2148 services requests per day. Therefore, we propose a Hybrid Optimization Algorithm that combines Genetic Algorithm and Space-Filling Curves to solve the Vehicle Route Problem. We establish the validity of the hybrid algorithm by performing tests in two different benchmarks datasets. Our proposal reached an average result of 12.7 percent and 4.1 percent better than the previous solutions in the first and second datasets respectively. Also, we compare our solution and five other variations of Ant Colony Optimization Algorithm. The results show that our proposal is superior in some simulations and, when it was not superior, presented the second-best results for almost all instances.

References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 4,472 TCR
SCOPUS® Citations for all references: 5,156 TCR

Web of Science® Average Citations per reference: 154 ACR
SCOPUS® Average Citations per reference: 178 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-06-25 03:18 in 163 seconds.

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