A Study of Planning Strategies Based on the Greedy Algorithm and Karush-Kuhn-Tucker (KKT) Conditional Algorithm

In this paper, a field management model is constructed based on data analysis and modelling techniques to optimize the use efficiency of arable land resources. Firstly, the yield differences of crops under different arable land types were analysed by visualisation techniques, and a two-layer planning model was introduced for optimal decision-making. Using the greedy algorithm and the Karush-Kuhn-Tucker (KKT) conditional algorithm, the maximum yield problem was solved and the total profit was evaluated under different sales scenarios. To cope with market uncertainty, Monte Carlo simulation and Data Envelopment Analysis (DEA) models were used to analyse the impact of four major volatility factors on returns. Ultimately, the optimal planting strategy is proposed, aiming to improve the management efficiency of arable land resources and providing a theoretical basis and practical reference for computer applications in related fields.