AHLLNS: An Automated Algorithm for Multi-Objective Heterogeneous Agricultural Robot Operation Scheduling Problems

Advances in multi-robot technology have accelerated the development of smart agriculture, enabling tasks to be executed collaboratively with higher efficiency. In heterogeneous agricultural robots collaborative operation scheduling, fuzzy time window and matching constraints significantly increase the problem complexity. This paper proposes a multi-objective heterogeneous agricultural robot operation scheduling model with fuzzy service time window and matching constraints (MHROS_FT&M), aiming to optimize the total operation cost and service level. Given the NP-hard property of MHROS_FT&M, the hierarchical learning large neighborhood search algorithm (HLLNS) is developed. HLLNS incorporates the hierarchical reinforcement learning to enhance adaptability, a dynamic programming-based approach to improve service levels, and a sub-problem collaboration and mutation strategy to escape local optimum. By employing automated algorithm design technique to optimize 12 key parameters, the automated HLLNS (AHLLNS) is realized. In practical smart-farming scenarios, AHLLNS supports the joint scheduling of heterogeneous robots such as spraying drones, weeding robots, and seeding drones under uncertain service times, and explicitly balances operation cost against farmer satisfaction. The obtained schedules reduce unnecessary travel and resource consumption while keeping service times within acceptable ranges for farmers. Through automatic parameter tuning and the use of problem-specific operators, AHLLNS effectively addresses fuzzy time windows and matching constraints, achieving better performance across different problem scales. Experimental comparisons with Gurobi and state-of-the-art algorithms demonstrate AHLLNS superior computational efficiency and solution quality, validating its effectiveness for MHROS_FT&M.