Using hybrid algorithm to reduce non-working distance in intra- and inter-field logistics simultaneously for heterogeneous harvesters

Reducing the non-working distance for agricultural vehicles is beneficial for reducing the total operational costs for farmers. In this study, we optimize the non-working distance in intra- and inter-fields simultaneously for harvesters while harvesting rice. A mixed integer programming is presented for optimizing the rice harvesting problem, where a similar working time for all harvesters are considered based on a real case. Besides, in this model, two kinds of fields, namely moist and non-moist fields are considered, where the first can only be harvested by crawler harvesters while the second can be harvested by any type of harvester. This model aims to find an optimal schedule for the harvesters. A hybrid algorithm using both adaptive large neighborhood search and tabu search techniques is implemented to optimize the problem. A case study is considered to verify the performance of the hybrid algorithm. The results indicate that the hybrid algorithm is an effective method for solving the rice harvesting problem because the total non-working distance could be improved significantly by 15.3%. The proportion of moist fields is analyzed because it influences the schedule significantly. When compared to the traditional method for optimizing the non-working distance for harvesters, the total non-working distance in this method could be reduced by 6.2%. This paper provides farmers with a general framework for reducing the non-working distance in intra- and inter-field logistics, which results in a more efficient performance of the harvesters in the fields.