Optimization Model for Crops Planting

Abstract

This study proposes a hybrid optimization model combining the Genetic Particle Swarm Algorithm (GPSO) with linear programming to optimize crop planting strategies under varying market conditions. The model incorporates price elasticity to estimate sales volumes and evaluates two scenarios: excess production leading to waste and excess production sold at a discount. By analyzing the results, our paper identifies optimal planting strategies for grains, legumes, vegetables, and edible fungi. Our findings reveal that crops with stable market demand, such as grains, are more suitable under conditions of excess waste, while high-value crops like edible fungi are preferable when excess production is sold at a discount. This research provides practical insights for agricultural planning, emphasizing the importance of market dynamics and crop-specific economic benefits.