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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Main Authors: C. Gracia, B. Diezma-Iglesias, P. Barreiro
Format: Article
Language:English
Published: Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria 2013-06-01
Series:Spanish Journal of Agricultural Research
Subjects:
Online Access:http://revistas.inia.es/index.php/sjar/article/view/3635
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spelling 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
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