Journal of Engineering, Project, and Production Management, 2026, 16(7), 2025-342
A Hybrid Multi-Objective Optimization Framework for Crop Planting Planning under Digital Economy
Deputy Director, Center for Contemporary Marxist Economics Research, Zhengzhou Technology and Business University, 451400, China, E-mail: wang_xhong@outlook.com
Project Management
Received December 18, 2025; revised February 4, 2026; accepted July 12, 2026
Available online July 25, 2026
Abstract: With the deep integration of artificial intelligence and the digital economy in agriculture, traditional crop planting planning still suffers from slow convergence, limited optimization accuracy, and weak adaptability to ecological and market uncertainties. To address these challenges, this study proposes a multi-objective crop planting optimization model that integrates the Multi-Objective Grey Wolf Optimizer (MOGWO), the non-dominated sorting genetic algorithm II, and proximal policy optimization, enabling dynamic decision-making driven by multi-source data. Experimental results on two authoritative datasets showed that the model achieved Pareto frontier coverage above 90% and stable convergence within 500 iterations. Multi-scenario simulations indicated that peak annual net profit exceeded 1.8 million yuan, and annual carbon reduction potential generally exceeded 150 tons of CO2, while computation time was maintained at approximately 4.8s per 100 iterations. Overall, the proposed model demonstrates strong robustness, ecological effectiveness, and computational efficiency, supporting intelligent agricultural decision-making under the digital economy framework.
Keywords: Digital economy; multi-objective optimization; MOGWO; crop planting planning; ecological benefits.
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Citation: Wang, X. (2026). A Hybrid Multi-Objective Optimization Framework for Crop Planting Planning under Digital Economy. Journal of Engineering, Project, and Production Management, 16(7), 2025-342.
