Multi-Objective Optimization of Greenhouse Operation Scenarios within the Water-Energy-Food-Ecosystem Nexus using a Genetic Algorithm

Document Type : Research Paper

Authors

1 Department of Water Engineering, Faculty of Agricultural Engineering and Technology (Aburayhan), University of Tehran, Tehran, Iran.

2 Department of Energy Systems Engineering, Faculty of Energy Engineering, Sharif University of Technology, Tehran, Iran.

10.22059/jwim.2026.410122.1286

Abstract

Optimal resource management in greenhouses requires quantitative and integrated approaches. This study presents a modeling-optimization framework to identify optimal operational configurations within the Water-Energy-Food-Ecosystem (WEFE) nexus. Field data from ten greenhouses in the Pakdasht region (40 annual observations from 2021-2024) were collected. First, a semi-empirical parametric model was developed and calibrated using this data to establish quantitative relationships between key decision variables (the share of water treated by Reverse Osmosis (RO) and the share of renewable energy) and five WEFE objective functions: minimizing water consumption, energy use, CO₂ emissions, and total cost, while maximizing a water quality index. Subsequently, a multi-objective genetic algorithm (NSGA-II) was executed on this calibrated model to extract the full Pareto fronts of optimal solutions. Finally, the TOPSIS multi-criteria decision-making method was employed to select the ultimate operational point from among the Pareto solutions. The optimization results identified a specific operational configuration utilizing RO for approximately 65 percent of the water flow and supplying 25 percent of energy from renewable sources as achieving the best trade-off (TOPSIS score: 0.7854) among the conflicting objectives. Compared to a conventional baseline, This optimum point, compared to a conventional baseline, resulted in a 24 percent reduction in water consumption, 20 percent in energy consumption, 33 percent in operating costs, and a 30 percent improvement in water quality. These findings demonstrate the strategic priority of increasing resource efficiency through technologies such as RO over simply expanding the clean energy portfolio and justify the “efficiency first, renewables then” strategy in similar regional conditions. This study provides a powerful tool for evidence-based, quantitative decision-making in sustainable agricultural management.

Keywords

References

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Water and Irrigation Management
Volume 15, Issue 4
March 2026
Pages 669-695

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