This study proposes a generalizable and extensible framework for task allocation among multiple agricultural machines. Although several previous studies have focused on specific aspects, such as route planning and task scheduling under constrained conditions, few have addressed the combined challenges of task division, variability in farmland scale, and algorithm selection with hyperparameter tuning in an integrated manner. To fill this gap, we formulate the problem as a split delivery vehicle routing problem, which enables flexible division of field tasks across machines. Based on this formulation, we construct a unified framework that incorporates farmland modeling, machine modeling, and farmer-specific preferences. The proposed framework is designed to accommodate multiple optimization algorithms such as simulated annealing, local search, genetic algorithm, and ant colony optimization under a common structure, allowing flexible applications across diverse agricultural scenarios. We evaluated the performance and sensitivity of the algorithm to the hyperparameters using simulations for varying farmland sizes and computation times. The results demonstrate that the framework effectively supports algorithm selection and parameter tuning according to situational needs. This approach offers a versatile foundation for optimizing agricultural tasks, and can be extended to dynamic and real-time environments using real farmland data.
Hayakawa et al. (Sun,) studied this question.