Adaptive Genetic Algorithm Integrated with Ant Colony Optimization for Multi-Task Agricultural Machinery Scheduling

Li Dai, Zhikai Jin, Xiong Zhao, Xiaoqiang Du and Zenghong Ma ()
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Li Dai: School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China
Zhikai Jin: School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou 310018, China
Xiong Zhao: School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Xiaoqiang Du: School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China
Zenghong Ma: School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China

Agriculture, 2025, vol. 15, issue 22, 1-23

Abstract: Efficient scheduling of agricultural machinery is critical for optimizing resource utilization and reducing operational costs in modern farming operations. This study proposes an Adaptive Genetic Algorithm integrated with Ant Colony Optimization (AGA-ACO) to solve the multi-task machinery scheduling problem. The problem is formulated as a Vehicle Routing Problem with Time Windows (VRPTW), considering time constraints, machinery heterogeneity, and task dependencies. The AGA-ACO algorithm employs a two-phase optimization strategy: genetic algorithms for global exploration and ant colony optimization for local refinement through pheromone-guided search. Experimental evaluation using real-world agricultural data from Hangzhou demonstrates that AGA-ACO achieves cost reductions of 5.92–10.87% compared to genetic algorithms, 5.47–7.75% compared to ant colony optimization, and 6.23–9.51% compared to particle swarm optimization, while converging with fewer iterations. The algorithm maintains stable convergence and high robustness across different farmland scales, reducing computational time while preserving solution quality. A scheduling management system integrating IoT sensors, MQTT protocols, and GIS technologies validates the practical applicability of the proposed approach. This research provides a replicable framework for agricultural machinery optimization, contributing to the advancement of sustainable and precision agriculture.

Keywords: agricultural machinery scheduling; hybrid metaheuristic algorithm; vehicle routing problem; time window constraints; adaptive genetic algorithm; precision agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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