View in EDS

Three-dimensional capacitated route planning optimization using parallel computing for agricultural field involving obstacle.

Saved in:
Bibliographic Details
Title: Three-dimensional capacitated route planning optimization using parallel computing for agricultural field involving obstacle.
Authors: Khosravani Moghadam, Erfan, Nørremark, Michael, Zhou, Kun, Nilsson, René Søndergaard, Lausdahl, Kenneth Guldbrandt, Sørensen, Claus Aage Grøn
Source: Precision Agriculture; Dec2025, Vol. 26 Issue 6, p1-27, 27p
Abstract: Forage harvesting is a critical agricultural practice that ensures prompt crop collection to maintain nutritional quality and economic viability. However, operating heavy machinery on sensitive field areas can lead to soil compaction, which may subsequently reduce crop yields. This study presents a comprehensive evaluation of advanced optimization techniques for capacitated multi-objective route planning in agricultural fields. Two algorithms including Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a novel Stochastic Greedy Algorithm (SGA) are compared within a parallel computing environment designed to significantly reduce computation time. By leveraging an innovative approach to three-dimensional capacitated route planning, the proposed method eliminates traditional block-based around the obstacle for the first time, thereby expanding the solution space and enabling the exploration of track combinations across entire fields. Experimental results demonstrate that the SGA reduced non-working distance by up to 41%, fuel consumption by 13%, and soil compaction stress length by 5% in a small field (19 tracks), and by 36%, 12%, and 30%, respectively, in a large field (82 tracks). NSGA-II also improved performance compared to conventional methods but was less efficient in larger, more complex fields. These findings indicate that advanced optimization strategies can substantially enhance operational efficiency and reduce both resource consumption and soil compaction in capacitated agricultural operations. Future work will focus on extending the optimization framework to support multiple harvesters operating simultaneously, enabling coordinated route planning and further reducing total field coverage time. [ABSTRACT FROM AUTHOR]
Copyright of Precision Agriculture is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract:Forage harvesting is a critical agricultural practice that ensures prompt crop collection to maintain nutritional quality and economic viability. However, operating heavy machinery on sensitive field areas can lead to soil compaction, which may subsequently reduce crop yields. This study presents a comprehensive evaluation of advanced optimization techniques for capacitated multi-objective route planning in agricultural fields. Two algorithms including Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and a novel Stochastic Greedy Algorithm (SGA) are compared within a parallel computing environment designed to significantly reduce computation time. By leveraging an innovative approach to three-dimensional capacitated route planning, the proposed method eliminates traditional block-based around the obstacle for the first time, thereby expanding the solution space and enabling the exploration of track combinations across entire fields. Experimental results demonstrate that the SGA reduced non-working distance by up to 41%, fuel consumption by 13%, and soil compaction stress length by 5% in a small field (19 tracks), and by 36%, 12%, and 30%, respectively, in a large field (82 tracks). NSGA-II also improved performance compared to conventional methods but was less efficient in larger, more complex fields. These findings indicate that advanced optimization strategies can substantially enhance operational efficiency and reduce both resource consumption and soil compaction in capacitated agricultural operations. Future work will focus on extending the optimization framework to support multiple harvesters operating simultaneously, enabling coordinated route planning and further reducing total field coverage time. [ABSTRACT FROM AUTHOR]
ISSN:13852256
DOI:10.1007/s11119-025-10297-3