A Heuristic-Guided Genetic Algorithm for heterogeneous agricultural machinery scheduling with time windows

Abstract

Effective scheduling of heterogeneous agricultural machinery (e.g., harvesters, tractors, and seeders) is critical for timely operations during busy farming seasons. However, most existing work focuses on homogeneous agricultural machinery scheduling, which is relatively simpler than heterogeneous agricultural machinery scheduling. To address this, the paper provides the first formal definition and mathematical model of the Heterogeneous Agricultural Machinery Scheduling Problem with Time Windows (HAMSP-TW). The model uniquely integrates multiple practical constraints previously treated in isolation, including heterogeneous machinery coordination, spatial transfer times between dispersed plots, multi-process job sequences, and plot-specific earliest start time windows. To solve HAMSP-TW, this paper proposes a Heuristic-Guided Genetic Algorithm (HGA) with problem-driven components, i.e., a two-part encoding scheme capturing machinery assignment and operation sequencing, a hybrid rule-based initialization strategy seeding high-quality, diverse solutions, and a heuristic-guided mutation operator leveraging domain knowledge for effective search. Experiments on 18 instances (60–400 plots) show the proposed HGA approach significantly outperforms benchmark algorithms, with advantages particularly pronounced in large-scale scenarios. Ablation studies confirm the substantial individual contribution of each proposed component. Gantt chart analysis reveals that optimized schedules are able to reduce idle time and improve machinery utilization, directly translating to shorter operation completion times.