Optimisation of agricultural routing planning in field logistics with Evolutionary Hybrid Neighbourhood Search

The optimisation of the agricultural process has gained importance over the past years as a means of increasing harvest yield, reducing cost and time required to maintain and harvest the fields, and maintaining economic and environmental sustainability. This research focuses on agricultural routing planning (ARP) for farmers' fields. The objective is to minimise the intra-field distance of the agricultural machine(s) when traversing all tracks, using an Evolutionary Hybrid Neighbourhood Search (EHNS) to solve different scenario problems. To obtain datasets for the agricultural routing problem, we gathered data from previous publications describing different fields. A mathematical model representing the optimisation of these datasets is also provided. The experimental results conclude that EHNS can either out-perform or obtain the same best solution as other algorithms in the literature. Among 9 problem sets, this study could find for 56% of the cases an improved combination of tracks saving an average of 10.68% non-working distance compared to other algorithms. The results also show that EHNS successfully gets the best objective function and the fastest convergence speed compared with the published algorithms. (C) 2019 IAgrE. Published by Elsevier Ltd. All rights reserved.