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A decision support system based on an artificial multiple intelligence system for vegetable crop land allocation problem

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Abstract

This research focuses on the development of a novel artificial multiple intelligence system (AMIS), which is more flexible and effective than existing techniques for determining vegetable crop land allocation. Eight intelligence boxes (IBs) have been newly designed to serve as AMIS improvement tools presented in this study. Furthermore, a novel formula has been developed to efficiently select the appropriate IB for various types of problems. The developed method will be incorporated into a vegetable land allocation decision support system. The decision-making of the planning about land allocation for crops, including what to grow and what is in demand during specific periods, was performed while considering important factors such as production yield, crop planting and harvesting time, vegetable price fluctuations, and plant incompatibility, leading to a sustainable production system and achieving the highest prices and annual income. Moreover, the developed vegetable crop land allocation models yield the similarity of the average profit per area, so farmers could plan their crops accordingly. To solve the problem, a mathematical model was proposed to solve a small-sized problem, while a novel metaheuristic called the Artificial Multiple Intelligence System (AMIS) was applied to solve larger-sized problems. The computational results revealed that AMIS outperformed all other traditional methods used for comparison in this research. The solution of AMIS was higher in quality than traditional methods such as Differential Evolution (DE), Multi-Agent Simulated Quenching (MASQ), and Genetic Algorithm (GA) by 21.78, 16.38, and 22.79%, respectively.

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Acknowledgements

This research was supported by the Research and Graduate Studies KhonKaen University, Thailand, Faculty of Engineering, Khon Kaen University and the Department of Industrial Engineering, Ubon Ratchathani University, Thailand.

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Authors and Affiliations

  1. Artificial Intelligence Optimization SMART Laboratory, Department of Industrial Engineering, Faculty of Engineering, Ubon Ratchathani University, Ratchathani, Thailand

    Rapeepan Pitakaso

  2. Research Unit On System Modeling for Industry, Industrial Engineering Department, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand

    Kanchana Sethanan

  3. Nottingham University Business School, Nottingham, England, UK

    Kim Hua Tan

  4. Department of Operations, Data and Artificial Intelligence, EMLYON Business School, Écully, France

    Ajay Kumar

Authors
  1. Rapeepan Pitakaso
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  2. Kanchana Sethanan
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  3. Kim Hua Tan
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  4. Ajay Kumar
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Correspondence to Kanchana Sethanan.

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Pitakaso, R., Sethanan, K., Tan, K.H. et al. A decision support system based on an artificial multiple intelligence system for vegetable crop land allocation problem. Ann Oper Res (2023). https://doi.org/10.1007/s10479-023-05398-z

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