Authors : Amjad Aljuhani; Rahaf Baghdadi; Wafaa Alzahrani; Deemah Aljuhani

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/775r6fam

Scribd : https://tinyurl.com/ynf6hnw8

DOI : https://doi.org/10.38124/ijisrt/25may004

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The increasing demand for food production in dry regions requires innovative agricultural practices. Vertical farming presents a sustainable solution by optimizing space and resource utilization while addressing food security challenges. This study examines the feasibility of vertical farming in Saudi Arabia, using mathematical optimization to determine the most suitable locations for vertical farms in the country. A mixed-integer linear programming model was developed using AIMMS software, incorporating key parameters such as infrastructure compatibility, water availability, and energy consumptions. Data from government records and geospatial analysis were integrated to enhance model accuracy. The results identify seven optimal locations in five cities—Riyadh, Jeddah, Dammam, Tabuk, and Khamis Mushait—ensuring efficient lettuce production at minimized costs. Findings highlight the potential of vertical farming in urban settings, reducing water consumption and enhancing food accessibility. However, challenges such as high energy requirements and initial investment costs persist. Future recommendations include decentralized container-based farming, renewable energy integration, and advanced automation. By implementing these solutions, vertical farming can transition from a niche agricultural practice to a mainstream, sustainable solution for food security in Saudi Arabia. This research provides a strategic framework for policymakers and investors to promote sustainable urban agriculture in the Kingdom.

Keywords : Vertical Farming, Location Optimization, Saudi Arabia, Mixed-Integer Linear Programming, AIMMS Software.

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