A hybrid genetic algorithm for route optimization in the bale collecting problem
The bale collecting problem (BCP) appears after harvest operations in grain and other crops. Its solution defines the sequence of collecting bales which lie scattered over the field. Current technology on navigation-aid systems or auto-steering for agricultural vehicles and machines, is able to prov...
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Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria
2013-06-01
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doaj-6492057ddeb84460a49c822103a9462d2020-11-25T01:22:39ZengInstituto Nacional de Investigación y Tecnología Agraria y AlimentariaSpanish Journal of Agricultural Research2171-92922013-06-0111360361410.5424/sjar/2013113-36351822A hybrid genetic algorithm for route optimization in the bale collecting problemC. Gracia0B. Diezma-Iglesias1P. Barreiro2Departamento de Organización de Empresas. Universitat Politècnica de València, Camino Vera s/n, DOE, 46022 ValenciaUniversidad Politécnica de Madrid, ETSI Agrónomos, Ciudad Universitaria, 28040, MadridUniversidad Politécnica de Madrid, ETSI Agrónomos, Ciudad Universitaria, 28040, MadridThe bale collecting problem (BCP) appears after harvest operations in grain and other crops. Its solution defines the sequence of collecting bales which lie scattered over the field. Current technology on navigation-aid systems or auto-steering for agricultural vehicles and machines, is able to provide accurate data to make a reliable bale collecting planning. This paper presents a hybrid genetic algorithm (HGA) approach to address the BCP pursuing resource optimization such as minimizing non-productive time, fuel consumption, or distance travelled. The algorithmic route generation provides the basis for a navigation tool dedicated to loaders and bale wagons. The approach is experimentally tested on a set of instances similar to those found in real situations. In particular, comparative results show an average improving of a 16% from those obtained by previous heuristics.http://revistas.inia.es/index.php/sjar/article/view/3635precision agriculturelogisticswheat harvest |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
C. Gracia B. Diezma-Iglesias P. Barreiro |
spellingShingle |
C. Gracia B. Diezma-Iglesias P. Barreiro A hybrid genetic algorithm for route optimization in the bale collecting problem Spanish Journal of Agricultural Research precision agriculture logistics wheat harvest |
author_facet |
C. Gracia B. Diezma-Iglesias P. Barreiro |
author_sort |
C. Gracia |
title |
A hybrid genetic algorithm for route optimization in the bale collecting problem |
title_short |
A hybrid genetic algorithm for route optimization in the bale collecting problem |
title_full |
A hybrid genetic algorithm for route optimization in the bale collecting problem |
title_fullStr |
A hybrid genetic algorithm for route optimization in the bale collecting problem |
title_full_unstemmed |
A hybrid genetic algorithm for route optimization in the bale collecting problem |
title_sort |
hybrid genetic algorithm for route optimization in the bale collecting problem |
publisher |
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria |
series |
Spanish Journal of Agricultural Research |
issn |
2171-9292 |
publishDate |
2013-06-01 |
description |
The bale collecting problem (BCP) appears after harvest operations in grain and other crops. Its solution defines the sequence of collecting bales which lie scattered over the field. Current technology on navigation-aid systems or auto-steering for agricultural vehicles and machines, is able to provide accurate data to make a reliable bale collecting planning. This paper presents a hybrid genetic algorithm (HGA) approach to address the BCP pursuing resource optimization such as minimizing non-productive time, fuel consumption, or distance travelled. The algorithmic route generation provides the basis for a navigation tool dedicated to loaders and bale wagons. The approach is experimentally tested on a set of instances similar to those found in real situations. In particular, comparative results show an average improving of a 16% from those obtained by previous heuristics. |
topic |
precision agriculture logistics wheat harvest |
url |
http://revistas.inia.es/index.php/sjar/article/view/3635 |
work_keys_str_mv |
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1725126159041036288 |