Navigating route planning for multiple vehicles in multifield agriculture with a fast hybrid algorithm
Optimisation of route planning is becoming increasingly valuable aspect in agriculture. This study focuses on Agricultural Route Planning (ARP) in multifield areas (with a specific entrance point), incorporating several heterogeneous agricultural machines. The aim of this research is to improve the...
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| Vydáno v: | Computers and electronics in agriculture Ročník 212; s. 108021 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.09.2023
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| Témata: | |
| ISSN: | 0168-1699, 1872-7107 |
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| Abstract | Optimisation of route planning is becoming increasingly valuable aspect in agriculture. This study focuses on Agricultural Route Planning (ARP) in multifield areas (with a specific entrance point), incorporating several heterogeneous agricultural machines. The aim of this research is to improve the route planning of (semi-)autonomous machines by producing more efficient route plans. The problem sets of ARP contain both medium and large numbers of tracks consisting of irregular and rectangular fields. This research proposes a Fast Hybrid Algorithm (FHA) to address this problem. FHA incorporates various combinatorial operators into its structure. The experimental results demonstrate that, compared to Tabu Search, (Improved) Genetic Algorithm and Ant Colony Optimisation, FHA can reduce the distance travelled by an average of 16.21%. Furthermore, the efficiency of FHA is also reflected in its running time, which saves up to 54.23% compared to the other methods.
•A new and fast hybrid algorithm (FHA) is developed for agricultural route planning.•The problem includes multiple fields with barriers and heterogeneous machines.•FHA reduced runtime while maintaining a minimal nonworking distance in the field. |
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| AbstractList | Optimisation of route planning is becoming increasingly valuable aspect in agriculture. This study focuses on Agricultural Route Planning (ARP) in multifield areas (with a specific entrance point), incorporating several heterogeneous agricultural machines. The aim of this research is to improve the route planning of (semi-)autonomous machines by producing more efficient route plans. The problem sets of ARP contain both medium and large numbers of tracks consisting of irregular and rectangular fields. This research proposes a Fast Hybrid Algorithm (FHA) to address this problem. FHA incorporates various combinatorial operators into its structure. The experimental results demonstrate that, compared to Tabu Search, (Improved) Genetic Algorithm and Ant Colony Optimisation, FHA can reduce the distance travelled by an average of 16.21%. Furthermore, the efficiency of FHA is also reflected in its running time, which saves up to 54.23% compared to the other methods.
•A new and fast hybrid algorithm (FHA) is developed for agricultural route planning.•The problem includes multiple fields with barriers and heterogeneous machines.•FHA reduced runtime while maintaining a minimal nonworking distance in the field. Optimisation of route planning is becoming increasingly valuable aspect in agriculture. This study focuses on Agricultural Route Planning (ARP) in multifield areas (with a specific entrance point), incorporating several heterogeneous agricultural machines. The aim of this research is to improve the route planning of (semi-)autonomous machines by producing more efficient route plans. The problem sets of ARP contain both medium and large numbers of tracks consisting of irregular and rectangular fields. This research proposes a Fast Hybrid Algorithm (FHA) to address this problem. FHA incorporates various combinatorial operators into its structure. The experimental results demonstrate that, compared to Tabu Search, (Improved) Genetic Algorithm and Ant Colony Optimisation, FHA can reduce the distance travelled by an average of 16.21%. Furthermore, the efficiency of FHA is also reflected in its running time, which saves up to 54.23% compared to the other methods. |
| ArticleNumber | 108021 |
| Author | Utamima, Amalia Reiners, Torsten |
| Author_xml | – sequence: 1 givenname: Amalia orcidid: 0000-0001-8203-4148 surname: Utamima fullname: Utamima, Amalia email: amalia@is.its.ac.id organization: Department of Information Systems, Institut Teknologi Sepuluh Nopember, Indonesia – sequence: 2 givenname: Torsten surname: Reiners fullname: Reiners, Torsten organization: Curtin University, Australia |
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| CitedBy_id | crossref_primary_10_1016_j_compag_2024_109628 crossref_primary_10_3390_agriculture15111125 crossref_primary_10_3390_land14071464 crossref_primary_10_3390_agriculture15121297 crossref_primary_10_1016_j_compag_2025_110207 crossref_primary_10_1016_j_cor_2025_107252 crossref_primary_10_1016_j_compag_2024_109217 crossref_primary_10_3390_agronomy15051129 |
| Cites_doi | 10.1002/rob.20403 10.1016/j.compag.2015.10.012 10.1016/j.compag.2022.106995 10.3390/agriengineering2030028 10.1016/j.jnca.2020.102972 10.1016/j.biosystemseng.2018.04.003 10.1016/j.biosystemseng.2016.10.007 10.1016/j.biosystemseng.2015.07.002 10.1016/j.ejor.2016.01.043 10.1002/rob.20300 10.1016/j.biosystemseng.2019.08.001 10.1016/j.compag.2020.105401 10.13031/2013.29488 10.1016/j.compag.2017.03.001 10.5424/sjar/2013113-3865 10.1016/j.compag.2014.12.024 10.1016/j.eswa.2018.08.008 10.1016/j.compag.2013.09.008 10.1016/j.biosystemseng.2008.06.008 10.1016/j.biosystemseng.2014.07.012 10.1016/B978-155860734-7/50097-4 10.1016/j.biosystemseng.2018.01.006 10.1016/j.compag.2020.105733 10.1007/s10479-022-04685-5 10.1016/j.compag.2018.12.002 10.1016/j.procs.2019.11.156 10.1016/j.compag.2020.105295 10.1016/j.compag.2016.06.012 10.1016/j.biosystemseng.2020.08.007 10.1016/j.compag.2016.11.010 10.1016/j.compag.2020.105273 |
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| Keywords | Multimachine Fast hybrid algorithm Tabu search Genetic algorithm Multifields Ant colony optimisation Agriculture Route planning |
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| SubjectTerms | Agriculture algorithms Ant colony optimisation electronics Fast hybrid algorithm Genetic algorithm Multifields Multimachine Route planning Tabu search |
| Title | Navigating route planning for multiple vehicles in multifield agriculture with a fast hybrid algorithm |
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