Abstract
Optimizing greenhouse operations in arid regions is essential for sustainable agriculture due to limited water resources and high energy demands for climate control. This paper proposes a multi-objective optimization framework aimed at minimizing both the operational costs and environmental emissions of a climate-controlled greenhouse. The framework determines optimal allocation of growing area among three crops (tomato, cucumber, and bell pepper) throughout the year. These crops were selected for their varying growth conditions, which induce variability in energy and water inputs, providing a comprehensive assessment of the optimization model. The model integrates factors such as temperature, humidity, light intensity, and irrigation requirements specific to each crop. It is solved using a genetic algorithm combined with Pareto front analysis to address the multi-objective nature effectively. This approach facilitates the identification of optimal trade-offs between cost, emissions, and yield, offering a set of efficient solutions for decision-makers. Applied to a greenhouse in Qatar, the model evaluates two scenarios: a hydroponic system and a conventional soil-based system. Results indicate that the multi-objective optimization effectively reduces operational costs and environmental emissions while fulfilling crop demand.The hydroponic scenario demonstrates higher water-use efficiency, resulting in lower overall emissions despite higher initial setup expenses. The soil-based system shows higher emissions due to increased fertilizer use and water consumption. The optimized allocation balances multiple crops simultaneously throughout the year based on seasonal conditions, enhancing overall sustainability. This study underscores the potential of advanced optimization techniques in enhancing the efficiency and sustainability of greenhouse agriculture in challenging environments.