A Linguistic MCDM Approach to Overcome Future Challenges of Vertical Farming

Food production is an essential operation where production and resource efficiency are low compared to other sectors. With the unstoppable growth of the world population, agricultural production is under pressure to meet the increasing food demand. Controlled Environment Agriculture (CEA) is a successful solution to create sustainable and resilient development through sustainable cities. CEA, where the farming activities are isolated from the meteorological conditions, is one of the most powerful solutions to adapt and mitigate climate change in urban areas. Vertical farming (VF) is also an indoor plant manufacturing process. In VF, plants are grown in layers and can thus reach high. The system can entirely be designed without any dependence on sunlight or other outdoor resources. However, there are a significant number of drawbacks about VF in the literature, such as limited products and labor costs, etc. This study focused on generating the VF area’s main challenges and wanted to create a roadmap to overcome these challenges. Existing VF challenges are gathered from experts and related literature. Possible solutions to overcome these limitations are derived from the literature as well. The process is approached as a multi-criteria decision-making (MCDM) procedure. The House of Quality (HoQ) of Quality Function Deployment (QFD) is suggested to investigate the relationships between solutions and challenges. The HoQ method also allows for prioritizing the potential solutions to generate a roadmap for practitioners. Plus, the methodology extends the QFD model with the 2-tuple linguistic model to overcome the vagueness by supplying linguistic sets to decision-makers (DMs) to assess via semantics closer to the human cognitive process. That helps to improve the accuracy of the linguistic computations and interpretability of the results. Also, it creates a flexible environment for DMs. A case study is applied for Turkey, and sensitivity analyses are presented to test the suggested methodology’s robustness.