Development of Rice Seedling Bank Location Method using Fuzzy-VIKOR-TOPSIS Hybrid Approach

Articles in Press

Document Type : Research Article

Authors

• M. Zangeneh ¹
• M. Rouhi Farajabad ²

¹ Biosystems Engineering Department, University of Guilan, Rasht, Iran

² Economic and Rural Development Department, University of Guilan, Rasht, Iran

https://doi.org/10.22067/jam.2023.83155.1175

Abstract

Introduction
To successfully provide and distribute agricultural services throughout the supply chain and enhance efficiency in this sector, selecting the right locations for service centers is a crucial and complex challenge. One of the ways to develop rice mechanization infrastructure is to establish rice seedling banks. A rice seedling bank is a specialized facility dedicated to the large-scale industrial production of rice seedlings, utilizing seedling trays to optimiz space, resources, and labor. The primary aim of this research is to identify the most suitable location for establishing a rice seed bank by employing multiple-criteria decision-making (MCDM) methods.
Materials and Methods
The present research was conducted in Fuman County, Guilan Province, Iran. The main objective of identifying a location for the seedling bank in the studied area is to minimize transportation costs for the seedling trays while selecting a site with the greatest potential for successfully establishing the seedling bank. To achieve this, we analyzed the location criteria for the seedling bank at the district level during the early stages of the research. The selection criteria for identifying a suitable district include several factors, such as the number of farmers, land leveling, area under cultivation, the number of agricultural machines, the level of mechanized transplanting and harvesting, and the number of seed banks in each district. Subsequently, the best village in the district, chosen in the prior step, was evaluated using several key criteria: total cultivated area, number of farmers, cultivated area per farmer, and total distance from other villages within the district. Shannon's entropy method was employed to estimate the weight and rank for the location criteria in both stages. The districts were ranked using the Fuzzy VIKOR method, while the TOPSIS method was used to prioritize villages within the selected district.
Results and Discussion
According to the results of the Fuzzy VIKOR method, among the five studied districts in Fuman County, Lulaman rural district stands out as the best location for establishing a seedling bank. Furthermore, based on the results obtained from the TOPSIS method, Khoshknudhan-e Bala village is identified as the most favorable site for establishing a seedling bank within the Lulaman district, among the fifteen alternatives considered. The VIKOR model excels in ranking alternatives due to its ability to generate ideal positive and negative maps, making it particularly well-suited for location and spatial analysis. By utilizing this model, we can assess not only the locations themselves but also evaluate how each alternative measures up against both positive and negative ideals. In contrast, other models lack this capability, as they merely identify the optimal location without providing a comprehensive understanding of each alternative's standing.
Conclusion
The purpose of this research is to provide a suitable algorithm for locating a seedling bank in Fuman County. Given the numerous influencing factors and available options, the integration of the VIKOR MCDM model with fuzzy numbers to identify the most suitable district, followed by the TOPSIS MCDM model to determine the best village, yielded promising results. The findings indicate that several factors play a crucial role in identifying the optimal location for the seedling bank. However, integrating all these elements through traditional methods—such as manual map analysis—proves impractical due to the sheer volume of data involved. Furthermore, neglecting these factors in site selection leads to substantial waste of material resources, energy, and environmental resources. Overall, the results of the Fuzzy VIKOR analysis revealed that Khoshknudhan-e Bala village in the Lulaman district is the best option for establishing a seedling bank in Fuman County.

Keywords

• Fuzzy VIKOR
• Geographical Information System
• Rice Seedling Bank Location
• Shannon Entropy
• TOPSIS

Main Subjects

• Agricultural systems engineering (greenhouse, fish farming, mushroom production)

©2024 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)  

References

1. Alikhah Asl, M., Moameri, M., Naseri, D., & Meftahi, Sh. (2021). Land suitability classification for development of Ziziphora tenuior using ANP Fuzzy at Ardabil province, Iran. Journal of Rangeland, 15(1), 156-167.
2. Amin Nayeri, B., & Peyvastehgar, Y. (2020). Pattern analysis using fuzzy VIKOR in locating educational facilities district Case Study: District 14 of Tehran Metropolis. Applied Research of Geographic Sciences (geographical sciences), 20(56), 241-260. (In Persian). https://doi.org/10.29252/jgs.20.56.241
3. Anonymous. (2017). Summary of the results of the agriculture statistics plan. Tehran, Iran. (In Persian).
4. Ayyildiz, E., & Taskin, A. (2022). A novel spherical fuzzy AHP-VIKOR methodology to determine serving petrol station selection during COVID-19 lockdown: A pilot study for İstanbul. Socio-Economic Planning Science, 83, 101345. https://doi.org/10.1016/j.seps.2022.101345
5. Babajani, A., Kalantari, K., Rezvanfar, A., & Shabanali Fami, H. (2009). Location Allocation of Horticultural Processing Industries in Kermanshah Province by Using Indexing Method and Geographical Information System (GIS). Iranian Journal of Biosystems Engineering, 40(1), 71-78. (In Persian).
6. Gholipoor, P., Rahmati, S. H., & Mozaffari, M. M. (2021). Ranking of Indicators Effective on the Locating Industrial Estate with TOPSIST-VIKOR Techniques (Case Study: Qazvin Province). Journal of Studies of Human Settlements Planning, 16(3), 509-522. (In Persian).
7. Hwang, C. L. & Yoon, K. (1981). Multiple attribute decision making– methods and applications: a state-of-the-art survey. New York, Springer.
8. Ji, C. F., Ma, X. Zhu, J., & Wang, S. (2020). Fault Propagation Path Inference in a Complex Chemical Process Based on Time-delayed Mutual Information Analysis. Computer Aided Chemical Engineering, 48, 1165-1170. https://doi.org/10.1016/B978-0-12-823377-1.50195-6
9. Khosravi, M., Raeisi, R., Almodaresi, S. A., & Esteqlal, A. (2020). Daily Market Location Using GIS and FUZZY Logic (Case Study: Sirjan). Urban Social Geography, 7,2, 113-128. (In Persian). https://doi.org/10.22103/JUSG.2020.2022
10. Keykha, E., Fakhire, A., Roohani, H., & Behmanesh, B. (2020). Land suitability classification for pit-seeding project using fuzzy multi-criteria decision making. Journal of Rangeland, 14(4), 657-672.
11. Mansouri, A., & Bagheri. A. (2015). Evaluating the Financial Performance and Ranking the Pharmaceutical Companies Listed on the Tehran Stock Exchange by Using Hybrid Approaches of Fuzzy Analytic Hierarchy Process and VIKOR. Journal of Health Accounting, 4(3), 86-107. (In Persian). https://doi.org/10.30476/jha.2015.17126
12. Mohammadi, M., & Sobhani, B. (2022). Location of water extraction systems from humidity using AHP/ Fuzzy in Sistan and Baluchestan province. Water and Soil Science, 32(2), 81-96. https://doi.org/10.22034/ws.2021.41900.2382
13. Motamed, M. K., & Kavoosi Kalashami, M. (2018). Evaluation of causal relationship between mechanization development and paddy production in Guilan province: Application of panel data and Granger-casual model. Cereal Research, 8(1), 1-13. (In Persian). https://doi.org/10.22124/c.2018.8584.1337
14. Oztaysi, B. (2014). A decision model for information technology selection using AHP integrated TOPSIS-Grey: The case of content management systems. Knowledge Based Systems, 70, 44-54. https://doi.org/10.1016/j.knosys.2014.02.010
15. Roy, B. (2004). Multiple Criteria Decision Analysis: State of The Art Surveys, Springer.
16. Safari, H., Faghih, A. R., & Fathi, M. R. (2012). Fuzzy multi-criteria decision making method for facility location selection. African Journal of Business Management, 6(1), 206-212. https://doi.org/10.5897/AJBM11.1760
17. Shahbandarzadeh, H., & Kabgani, M. (2021). Assessing Operational Efficiency of Banks via Quantitative Analysis of ATM Location Selection Criteria. Journal of Productivity Management, 15(1), 189-216. (In Persian). https://doi.org/10.30495/qjopm.2021.524296.1265
18. Shakouri, H., Nabaee, M., & Aliakbarisani, S. (2014). A quantitative discussion on the assessment of power supply technologies: DEA (data envelopment analysis) and SAW (simple additive weighting) as complementary methos for the "Grammar", Energy, 64, 640-647. https://doi.org/10.1016/j.energy.2013.10.022
19. Shemshadi, A., Shirazi, H., Toreihi, M., & Tarokh, M. J. (2011). A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Systems with Applications, 38(10), 12160-12167. https://doi.org/10.1016/j.eswa.2011.03.027
20. Wu, Y., Zhang, B., Xu, C., & Li, L. (2018). Site selection decision framework using fuzzy ANP-VIKOR for large commercial rooftop PV system based on sustainability perspective. Sustainable Cities and Society, 40, 454-470. https://doi.org/10.1016/j.scs.2018.04.024
21. Zangeneh, M., & Akram, A. (2020). Developing Approaches for Locating and Creating Agricultural Service Centers using Location-Allocation Model. Agricultural Mechanization and System Research, 74, 185-202. (In Persian). https://doi.org/10.22092/erams.2020.126188.1309