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

Rapeepan Pitakaso (), Kanchana Sethanan (), Kim Hua Tan () and Ajay Kumar ()
Additional contact information
Rapeepan Pitakaso: Ubon Ratchathani University
Kanchana Sethanan: Khon Kaen University
Kim Hua Tan: Nottingham University Business School
Ajay Kumar: Department of Operations, Data and Artificial Intelligence, EMLYON Business School

Annals of Operations Research, 2024, vol. 342, issue 1, No 19, 656 pages

Abstract: 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.

Keywords: OR in agriculture; Artificial multiple intelligence system; Cooperatives crop planning; AMIS embedded decision support system; Modified differential evolution algorithm; Multi agent simulated quenching (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05398-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:342:y:2024:i:1:d:10.1007_s10479-023-05398-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05398-z

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().