Optimizing Agricultural Decision-Making with Integrated MCDM-MCDA Methods: A Case Study on Crop Economics
Abstract
Because of the quantitative and qualitative uncertainty and complexity, it is not sufficient to use just Multicriteria Decision Making (MCDM), but also Multi-Criteria Decision Analysis (MCDA). The MCDM relates to the methods by which decisions are taken (i.e., selection of alternatives, ranked or ordered, and for what is analyzed being objective values. While MCDA offers a comprehensive approach for systematic assessment of criteria considering its impact on decision outcomes. Given that both methods have their own strengths, it is necessary to apply both MCDM and MCDA in agricultural economics which has a lot of uncertainty because of market price variability, increasing input costs and changing weather patterns. In this paper, Fuzzy Hypersoft Sets (FHSs), is used to model this problem and a case study is solved with Stable Preference Ordering Towards Ideal Solution (SPOTIS), Random Forest (RF), and Multi-Objective Optimization by Ratio Analysis (MULTIMOORA) to identify the most favorable crop for Jane's farm in terms of weather, costs required during agricultural production process like water or land usage, pesticide resistance against pests as well as market demand. as an alternative A7 = 0.526 was identified as the best choice by all three methods with Tomatoes and Rice scoring second, based on calculated score values. Thus, it enables us to study both quantitative and qualitative data, making it extremely able for agriculture uncertainties. This unique usage of sophisticated mathematics integrated with machine learning allows the decision-makers to find more accurate results, meaning it can manage economic risks better and allocate resources intelligently in agriculture. The comparative analysis with existing studies highlights the superiority of proposed work. Thus, it is significantly superior in accuracy. Hence farmers can harness farm economics to address these challenges by managing economic risks using mathematical decisionmaking tool, thereby leading them towards sustainability of livelihoods, food security and a resilient agricultural sector.
References
A. Rastogi, P. Das, and P. Roy, “Precision agriculture: role of artificial intelligence, drone and IoT,” Journal of Pharmacognosy and Phytochemistry, vol. 9, no. 1, pp. 686-689, 2020.
J. A. Verreet, J. Kroschel, and W. Kroschel, “Decision-Making for Precision Crop Protection,” Journal of Plant Diseases and Protection, vol. 114, no. 5, pp. 188-193, 2007.
S. Y. Hong, Q. Zhang, J. Xu, and Y. J. Kwak, “Decision Making and Precision Agriculture Technologies in Crop Production: A Review,” Applied Sciences, vol. 8, no. 8, pp. 1255, 2018.
K. A. Mishra and A. D'Souza, “Crop diversification and farmers' income: Evidence from India,” Journal of Agribusiness in Developing and Emerging Economies, vol. 11, no. 2, pp. 138-153, 2021.
Y. Li, and L. G. Pederson, “Decision-Making in Crop Production Under Risk: A Stochastic Dominance Approach,” Canadian Journal of Agricultural Economics, vol. 62, no. 2, pp. 237253, 2014.
K. R. Sharma, P. S. Singh, and V. N. Mishra, “Decision Making in Crop Production Planning Using a Fuzzy Linear Programming Model,” International Journal of Agricultural and Statistical Sciences, vol. 13 no. 2, pp. 565-574, 2017.
S. Fountas, D. Wulfsohn, S. Blackmore, L. H. Jacobsen and M. S. Pedersen, “Sustainable Decision Making in Crop Production: A Multi-Criteria Analysis,” Biosystems Engineering, vol. 96, no. 2, pp. 200-212, 2007.
P. Juroszek and V. Witzke-Ehbrecht, “Decision-Making Processes in Crop Protection: A Review,” Plants, vol. 9, no. 8, pp. 951, 2020.
S. A. Wani, A. Mondal and A. S. Dar, “Application of Stochastic Optimization Techniques in Agriculture: A Review,” Indian Journal of Agricultural Economics, vol. 74, no. 4, pp. 570-580, 2019.
K. C. Knapp, A. K. Baerenklau, and A. K. Schwabe, “An Economic Decision Model for Farm-Level Water Management in Crop Production: Theory and Application,” American Journal of Agricultural Economics, vol. 92, no. 2, pp. 446-459, 2010.
P. Kumar and S. B. Chandel, “Impact of agricultural policies on crop choice in India: An empirical analysis,” Indian Journal of Agricultural Economics, vol. 73, no. 2, pp. 157-169, 2018.
L. A. Zadeh,“Fuzzy sets,” Information and control, vol. 8, no. 3, pp. 353-388, 1965.
S. Miyamoto, “Remarks on basics of fuzzy sets and fuzzy multisets,” Fuzzy Sets and Systems, vol. 156, no. 3, pp. 427-431, 2005.
J. Chen, S. Li, S. Ma and X. Wang, “M-Polar fuzzy sets: An extension of bipolar fuzzy sets,” The Scientific World Journal, vol. 2014, Article ID 416530, 2014. doi: 10.1155/2014/416530
R. W. Zhang, “Bipolar fuzzy sets and relations: A computational framework for cognitive modeling and multiagent decision analysis,” NAFIPS/IFIS/NASA '94. Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intellige, San Antonio, TX, USA, 305309, 1994. doi: 10.1109/IJCF.1994.375115.
B. Turksen and B. L. Turksen, “Interval valued fuzzy sets based on normal forms,” Fuzzy Sets and System, vol. 20, pp. 191-210, 1986. doi: 10.1016/0165-0114(86)90077-1
C. Kahraman, C. S. Onar and B. Oztaysi, “Fuzzy multicriteria decision-making: a literature review,” International Journal of Computer and Intelligent System, vol. 8, no. 4, pp. 637-666, 2015.
V. Torra, “Hesitant fuzzy sets,” International Journal of Intelligent Systems, vol. 25, no. 6, pp. 529-539, 2010.
D. Tadić, “Fuzzy multi-criteria approach to ordering policy ranking in a supply chain,” Yugoslav Journal of Operations Research, vol. 15, no. 2, pp. 243-258, 2005. https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/249
S. S. Mohammed, and A. Azam, “An Algorithm for Fuzzy Soft Set Based Decision-Making Approach,” Yugoslav Journal of Operations Research, [S.l.], vol. 30, no. 1, pp. 59-70, 2019. https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/776
K. Atanassov, “Intuitionistic Fuzzy Sets,” Fuzzy Sets and Systems, vol. 20, pp. 87-96, 1986. doi: 10.1016/S0165-0114(86)80034-3.
P. A. Ejegwa, “Pythagorean fuzzy set and its application in career placements based on academic performance using max-min-max composition,” Complex Intelligent Systems, vol. 5, no. 2, pp. 165-175, 2010. doi: 10.1007/s40747-019-0091-6.
X. Zhang and Z. Xu, “Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets,” International Journal of Intelligent Systems, vol. 29, no. 12, pp. 1061-1078, 2014. doi: 10.1002/int.21676.
R. Kumar, S. A. Edalatpanah, S. Jha, and R. Singh, "A Pythagorean fuzzy approach to the transportation problem," Complex & intelligent systems, vol. 5, no. 2, pp. 255-263, 2019.
F. Smarandache, Neutrosophy: Neutrosophic Probability, Set, and Logic: Analytic Synthesis & Synthetic Analysis. Rehoboth, NM: American Research Press. 1998.
D. Molodtsov, “Soft set theory—First results,” Computers and Mathematics with Applications, vol. 37, no. 4-5, pp. 19–31, 1999.
P. K. Maji, R. Biswas and A. R. Roy, “Fuzzy soft sets,” The Journal of Fuzzy Mathematics, vol. 9, pp. 589–602, 2001.
P. K. Maji and A. R. Roy, “An application of soft sets in a decision-making problem,” Computers & Mathematics with Applications, vol. 44, no. 8-9, pp. 1077-1083, 2002.
F. Smarandache, “Extension of Soft Set to Hypersoft Set, and then to Plithogenic Hypersoft Set,” Neutrosophic Sets and System, vol. 22, pp. 168-170, 2018.
M. N. Jafar and M. Saeed, “Aggregation operators of Fuzzy hypersoft sets,” Turkish Journal of Fuzzy Systems, vol. 11, no. 1, pp. 1-17, 2021.
A. Yolcu and Y. T. Öztürk, “Fuzzy hypersoft sets and its application to decision-making,” In Theory and Application of Hypersoft Set, 138-154. Brussels, Belgium: Pons Publishing House, 2021. Extension
S. Debnath, “Fuzzy hypersoft sets and its weightage operator for decision making,” Journal of Fuzzy and Applications, 10.22105/jfea.2021.275132.1083 vol. 2, pp. 163-170, 2021. doi:
M. Saqlain, M. Riaz, N. Kiran, P. Kumam and M. S. Yang, “Water Quality Evaluation Using Generalized Correlation Coefficient for M-Polar Neutrosophic Hypersoft Sets,” Neutrosophic Sets and System, vol. 55, pp. 58-89, 2023. doi: 10.5281/zenodo.7832716
M. Saqlain, M. Riaz, R. Imran, and F. Jarad, “Distance and similarity measures of intuitionistic fuzzy hypersoft sets with application: Evaluation of air pollution in cities based on air quality index,” AIMS Mathematics, vol. 8, no. 3, pp. 6880-6899, 2023. doi: 10.3934/math.2023348
M. Saqlain, H. Garg, P. Kumam and W. Kumam, “Uncertainty and decision-making with multi-polar interval-valued neutrosophic hypersoft set: A distance, similarity measure, and machine learning approach,” Alexandria Engineering Journal, vol. 84, pp. 323-332, 2023. doi: 10.1016/j.aej.2023.11.001
K. A. Bagherzadeh, “Agribusiness through Smart Technology an Approach of Smart Agriculture.” Computational Algorithms and Numerical Dimensions, vol. 1, no.1, pp. 40-45, 2022.
A. Panda, S. A. Edalatpanah, & G. R. Karim, “Improve crop production through WSN: an approach of smart agriculture.” Big Data and Computing Visions, vol. 1, no. 2, pp. 71-82, 2021.
Z. Zhou, “Soil quality based agricultural activity through IoT and wireless sensor network.” Big Data and Computing Visions, vol. 3, no. 1, pp. 26-31, 2023.
S. Nourkhah, G. Cirovic, & S. A. Edalatpanah, “The role of sensors in smart agriculture.” Computational Algorithms and Numerical Dimensions, vol. 2, no. 4, pp. 210-215, 2023.
G. Yongyi, X. Kaijun, H. Xinyue, C. Mengting “Research on Improving the Financial Capacity of Farmers Based on Fuzzy Analytic Hierarchy Process.” Management Analytics and Social Insights, vol. 1, no. 1, pp.88-102, 2024.
S. Debnath, “Fuzzy hypersoft sets and its weightage operator for decision making.” Journal of Fuzzy Extension and Applications, vol. 2, no. 2, pp. 163-170, 2021.
X. Li, Y. Zhang, A. Sorourkhah, & S. A. Edalatpanah, “Introducing Antifragility Analysis Algorithm for Assessing Digitalization Strategies of the Agricultural Economy in the Small Farming Section.” Journal of the Knowledge Economy, vol. 15, pp. 12191–122152023. doi: 10.1007/s13132-023-01558-5
A. C. Thomaz, “A Short Study Role of Wireless Networks in Smart Agriculture.” Computational Algorithms and Numerical Dimensions, vol. 1, no. 4, pp. 155-158, 2022.
A. A. El-Douh, S. Lu, A. Abdelhafeez, A. M. Ali, A. S. Aziz, “Heart Disease Prediction under Machine Learning and Association Rules under Neutrosophic Environment.” Neutrosophic Systems with Applications, vol. 10, pp. 35-52, 2023.
R. Khaliq, P. Iqbal, S. Bhat, R. Singh, S. Jain, & P. Agarwal, “Generalized Hukuhara derivative approach for Gompertz tumor growth with allee effect under fuzzy environment.” Journal of Fuzzy Extension and Applications, vol. 4, no.4, pp. 246-256, 2023.
A. U. Rahman, “A theoretical context for (θ,β)-convexity and (θ,β)-concavity with hypersoft settings.” Big Data and Computing Visions, vol. 3, no. 4, pp. 196-208, 2023.
F. Smarandache, “Foundation of the SuperHyperSoft Set and the Fuzzy Extension SuperHyperSoft Set: A New Vision.” Neutrosophic Systems with Applications, vol. 11, pp. 48-51, 2023.
F. Smarandache, “Soft set product extended to hypersoft set and indetermsoft set cartesian product extended to indetermhypersoft set.” Journal of Fuzzy Extension and Applications, vol. 3, no. 4, pp. 313-316, 2022.
M. Imran, & N. Ali, “Exploring the Synergy Between Tourism and Economic Development in Pakistan.” Systemic Analytics, vol. 2, no. 1, pp. 48-58, 2024.
H. Taherdoost and M. Madanchian, “Multi-Criteria Decision Making (MCDM) Methods and Concepts,” Encyclopedia, vol. 3, no. 1, pp. 77-87, 2023. doi: 10.3390/encyclopedia3010006
Brauers, M. K. Willem and E. K. Zavadskas, “The MOORA method and its application to privatization in a transition economy,” Control and Cybernetics, vol. 35, pp. 445-469, 2006.
L. Breiman, "Random Forests." Machine Learning, vol. 45, no. 1, pp. 5-32, 2001. doi: 10.1023/A:1010933404324
M. Tsakalerou, D. Efthymiadis, A. Abilez, "An intelligent methodology for the use of multicriteria decision analysis in impact assessment: the case of real-world offshore construction." Scientific Reports, vol. 12, no. 1, pp. 15137, 2022.
Y. Chen, Y. Zhou, F. Shiqi, M. L. Y. Wang and K. Cao, "Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty," Science of The Total Environment, vol. 809, p. 151152, 2022.
M. Saqlain, P. Kumam, W. Kumam. “Uncertainty and Decision-Making in Crop Economics Using Fuzzy Hypersoft Set with MULTIMOORA Method and Machine Learning.” In: Ngoc Thach, N., Trung, N.D., Ha, D.T., Kreinovich, V. (eds) Partial Identification in Econometrics and Related Topics. Studies in Systems, Decision and Control, vol 531. Springer, Cham.
Q. Sun, J. Ren, and F. Zhao, "Sliding mode control of discrete-time interval type-2 fuzzy Markov jump systems with the preview target signal," Applied Mathematics and Computation, vol. 435, p. 127479, 2022. doi: 10.1016/j.amc.2022.127479
J. Ge and S. Zhang, "Adaptive Inventory Control Based on Fuzzy Neural Network under Uncertain Environment," Complexity, vol. 2020, no. 1, p. 6190936, 2019. doi: 10.1155/2020/6190936
N. Zhang, W. Qi, G. Pang, J. Cheng, and K. Shi, "Observer-based sliding mode control for fuzzy stochastic switching systems with deception attacks," Applied Mathematics and Computation, vol. 427, p. 127153, 2022. doi: 10.1016/j.amc.2022.127153
M. Sarwar, H., and T. Li, "Fuzzy fixed-point results and applications to ordinary fuzzy differential equations in complex valued metric spaces," Hacettepe Journal of Mathematics and Statistics, vol. 48, no. 6, pp. 1712-1728, 2019. doi: 10.15672/HJMS.2018.633
Y. Xia, J. Wang, B. Meng, and X. Chen, "Further results on fuzzy sampled-data stabilization of chaotic nonlinear systems," Applied Mathematics and Computation, vol. 379, p. 125225, 2020. doi: 10.1016/j.amc.2020.125225
M. Gao, L. Zhang, W. Qi, J. Cao, J. Cheng, Y. Kao, Y. Wei, and X. Yan, "SMC for semiMarkov jump T-S fuzzy systems with time delay," Applied Mathematics and Computation, vol. 374, p. 125001, 2020. doi: 10.1016/j.amc.2019.125001
M. Madić, G. Petrović, D. Petković, and P. Janković, "Traditional and Integrated MCDM Approaches for Assessment and Ranking of Laser Cutting Conditions," Spectrum of Mechanical Engineering and Operational Research, vol. 1, no. 1, pp. 250-257, 2024. doi: 10.31181/smeor11202422
M. Saqlain, "Sustainable Hydrogen Production: A Decision-Making Approach Using VIKOR and Intuitionistic Hypersoft Sets," Journal of Intelligent and Management Decision, vol. 2, no. 3, pp. 130-138, 2023. doi: 10.56578/jimd020303
M. Asif, U. Ishtiaq, and I. K. Argyros, "Hamacher Aggregation Operators for Pythagorean Fuzzy Set and its Application in Multi-Attribute Decision-Making Problem," Spectrum of Operational Research, vol. 2, no. 1, pp. 27-40, 2024. doi: 10.31181/sor2120258
H. B. U. Haq, & M. Saqlain, “Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach,”Journal of Industrial Intelligence, vol. 1, no. 3, pp. 136-147, 2023. doi: 10.56578/jii010301
S. Kousar, A. Ansar, N. Kausar, and G. Freen, "Multi-Criteria Decision-Making for Smog Mitigation: A Comprehensive Analysis of Health, Economic, and Ecological Impacts," Spectrum of Decision Making and Applications, vol. 2, no. 1, pp. 53-67, 2024. doi: 10.31181/sdmap2120258
J. Kannan, V. Jayakumar, and M. Pethaperumal, "Advanced Fuzzy-Based Decision-Making: The Linear Diophantine Fuzzy CODAS Method for Logistic Specialist Selection," Spectrum of Operational Research, vol. 2, no. 1, pp. 41-60, 2024. doi: 10.31181/sor2120259
W. Kumam, K. Khammahawong, M. J. Khan, T. Bantaojai and S. Phiangsungnoen, “Fermatean Fuzzy Divergences and Their Applications to Decision-making and Pattern Recognition,” Nonlinear Convex Analysis and Optimization: An International Journal on Numerical, Computation and Applications, vol. 2, no. 1, pp 31–53, 2023. doi: 10.58715/ncao.2023.2.2
M. S. Sezen, H. Isık, & D. Turkoglu, “A New Method for Some Fixed-Point Theorems in Fuzzy Metric Spaces,” Bangmod International Journal of Mathematical and Computational Science, vol. 3, pp. 10–15, 2017.
J. A. Verreet, J. Kroschel, and W. Kroschel, “Decision-Making for Precision Crop Protection,” Journal of Plant Diseases and Protection, vol. 114, no. 5, pp. 188-193, 2007.
S. Y. Hong, Q. Zhang, J. Xu, and Y. J. Kwak, “Decision Making and Precision Agriculture Technologies in Crop Production: A Review,” Applied Sciences, vol. 8, no. 8, pp. 1255, 2018.
K. A. Mishra and A. D'Souza, “Crop diversification and farmers' income: Evidence from India,” Journal of Agribusiness in Developing and Emerging Economies, vol. 11, no. 2, pp. 138-153, 2021.
Y. Li, and L. G. Pederson, “Decision-Making in Crop Production Under Risk: A Stochastic Dominance Approach,” Canadian Journal of Agricultural Economics, vol. 62, no. 2, pp. 237253, 2014.
K. R. Sharma, P. S. Singh, and V. N. Mishra, “Decision Making in Crop Production Planning Using a Fuzzy Linear Programming Model,” International Journal of Agricultural and Statistical Sciences, vol. 13 no. 2, pp. 565-574, 2017.
S. Fountas, D. Wulfsohn, S. Blackmore, L. H. Jacobsen and M. S. Pedersen, “Sustainable Decision Making in Crop Production: A Multi-Criteria Analysis,” Biosystems Engineering, vol. 96, no. 2, pp. 200-212, 2007.
P. Juroszek and V. Witzke-Ehbrecht, “Decision-Making Processes in Crop Protection: A Review,” Plants, vol. 9, no. 8, pp. 951, 2020.
S. A. Wani, A. Mondal and A. S. Dar, “Application of Stochastic Optimization Techniques in Agriculture: A Review,” Indian Journal of Agricultural Economics, vol. 74, no. 4, pp. 570-580, 2019.
K. C. Knapp, A. K. Baerenklau, and A. K. Schwabe, “An Economic Decision Model for Farm-Level Water Management in Crop Production: Theory and Application,” American Journal of Agricultural Economics, vol. 92, no. 2, pp. 446-459, 2010.
P. Kumar and S. B. Chandel, “Impact of agricultural policies on crop choice in India: An empirical analysis,” Indian Journal of Agricultural Economics, vol. 73, no. 2, pp. 157-169, 2018.
L. A. Zadeh,“Fuzzy sets,” Information and control, vol. 8, no. 3, pp. 353-388, 1965.
S. Miyamoto, “Remarks on basics of fuzzy sets and fuzzy multisets,” Fuzzy Sets and Systems, vol. 156, no. 3, pp. 427-431, 2005.
J. Chen, S. Li, S. Ma and X. Wang, “M-Polar fuzzy sets: An extension of bipolar fuzzy sets,” The Scientific World Journal, vol. 2014, Article ID 416530, 2014. doi: 10.1155/2014/416530
R. W. Zhang, “Bipolar fuzzy sets and relations: A computational framework for cognitive modeling and multiagent decision analysis,” NAFIPS/IFIS/NASA '94. Proceedings of the First International Joint Conference of The North American Fuzzy Information Processing Society Biannual Conference. The Industrial Fuzzy Control and Intellige, San Antonio, TX, USA, 305309, 1994. doi: 10.1109/IJCF.1994.375115.
B. Turksen and B. L. Turksen, “Interval valued fuzzy sets based on normal forms,” Fuzzy Sets and System, vol. 20, pp. 191-210, 1986. doi: 10.1016/0165-0114(86)90077-1
C. Kahraman, C. S. Onar and B. Oztaysi, “Fuzzy multicriteria decision-making: a literature review,” International Journal of Computer and Intelligent System, vol. 8, no. 4, pp. 637-666, 2015.
V. Torra, “Hesitant fuzzy sets,” International Journal of Intelligent Systems, vol. 25, no. 6, pp. 529-539, 2010.
D. Tadić, “Fuzzy multi-criteria approach to ordering policy ranking in a supply chain,” Yugoslav Journal of Operations Research, vol. 15, no. 2, pp. 243-258, 2005. https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/249
S. S. Mohammed, and A. Azam, “An Algorithm for Fuzzy Soft Set Based Decision-Making Approach,” Yugoslav Journal of Operations Research, [S.l.], vol. 30, no. 1, pp. 59-70, 2019. https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/776
K. Atanassov, “Intuitionistic Fuzzy Sets,” Fuzzy Sets and Systems, vol. 20, pp. 87-96, 1986. doi: 10.1016/S0165-0114(86)80034-3.
P. A. Ejegwa, “Pythagorean fuzzy set and its application in career placements based on academic performance using max-min-max composition,” Complex Intelligent Systems, vol. 5, no. 2, pp. 165-175, 2010. doi: 10.1007/s40747-019-0091-6.
X. Zhang and Z. Xu, “Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets,” International Journal of Intelligent Systems, vol. 29, no. 12, pp. 1061-1078, 2014. doi: 10.1002/int.21676.
R. Kumar, S. A. Edalatpanah, S. Jha, and R. Singh, "A Pythagorean fuzzy approach to the transportation problem," Complex & intelligent systems, vol. 5, no. 2, pp. 255-263, 2019.
F. Smarandache, Neutrosophy: Neutrosophic Probability, Set, and Logic: Analytic Synthesis & Synthetic Analysis. Rehoboth, NM: American Research Press. 1998.
D. Molodtsov, “Soft set theory—First results,” Computers and Mathematics with Applications, vol. 37, no. 4-5, pp. 19–31, 1999.
P. K. Maji, R. Biswas and A. R. Roy, “Fuzzy soft sets,” The Journal of Fuzzy Mathematics, vol. 9, pp. 589–602, 2001.
P. K. Maji and A. R. Roy, “An application of soft sets in a decision-making problem,” Computers & Mathematics with Applications, vol. 44, no. 8-9, pp. 1077-1083, 2002.
F. Smarandache, “Extension of Soft Set to Hypersoft Set, and then to Plithogenic Hypersoft Set,” Neutrosophic Sets and System, vol. 22, pp. 168-170, 2018.
M. N. Jafar and M. Saeed, “Aggregation operators of Fuzzy hypersoft sets,” Turkish Journal of Fuzzy Systems, vol. 11, no. 1, pp. 1-17, 2021.
A. Yolcu and Y. T. Öztürk, “Fuzzy hypersoft sets and its application to decision-making,” In Theory and Application of Hypersoft Set, 138-154. Brussels, Belgium: Pons Publishing House, 2021. Extension
S. Debnath, “Fuzzy hypersoft sets and its weightage operator for decision making,” Journal of Fuzzy and Applications, 10.22105/jfea.2021.275132.1083 vol. 2, pp. 163-170, 2021. doi:
M. Saqlain, M. Riaz, N. Kiran, P. Kumam and M. S. Yang, “Water Quality Evaluation Using Generalized Correlation Coefficient for M-Polar Neutrosophic Hypersoft Sets,” Neutrosophic Sets and System, vol. 55, pp. 58-89, 2023. doi: 10.5281/zenodo.7832716
M. Saqlain, M. Riaz, R. Imran, and F. Jarad, “Distance and similarity measures of intuitionistic fuzzy hypersoft sets with application: Evaluation of air pollution in cities based on air quality index,” AIMS Mathematics, vol. 8, no. 3, pp. 6880-6899, 2023. doi: 10.3934/math.2023348
M. Saqlain, H. Garg, P. Kumam and W. Kumam, “Uncertainty and decision-making with multi-polar interval-valued neutrosophic hypersoft set: A distance, similarity measure, and machine learning approach,” Alexandria Engineering Journal, vol. 84, pp. 323-332, 2023. doi: 10.1016/j.aej.2023.11.001
K. A. Bagherzadeh, “Agribusiness through Smart Technology an Approach of Smart Agriculture.” Computational Algorithms and Numerical Dimensions, vol. 1, no.1, pp. 40-45, 2022.
A. Panda, S. A. Edalatpanah, & G. R. Karim, “Improve crop production through WSN: an approach of smart agriculture.” Big Data and Computing Visions, vol. 1, no. 2, pp. 71-82, 2021.
Z. Zhou, “Soil quality based agricultural activity through IoT and wireless sensor network.” Big Data and Computing Visions, vol. 3, no. 1, pp. 26-31, 2023.
S. Nourkhah, G. Cirovic, & S. A. Edalatpanah, “The role of sensors in smart agriculture.” Computational Algorithms and Numerical Dimensions, vol. 2, no. 4, pp. 210-215, 2023.
G. Yongyi, X. Kaijun, H. Xinyue, C. Mengting “Research on Improving the Financial Capacity of Farmers Based on Fuzzy Analytic Hierarchy Process.” Management Analytics and Social Insights, vol. 1, no. 1, pp.88-102, 2024.
S. Debnath, “Fuzzy hypersoft sets and its weightage operator for decision making.” Journal of Fuzzy Extension and Applications, vol. 2, no. 2, pp. 163-170, 2021.
X. Li, Y. Zhang, A. Sorourkhah, & S. A. Edalatpanah, “Introducing Antifragility Analysis Algorithm for Assessing Digitalization Strategies of the Agricultural Economy in the Small Farming Section.” Journal of the Knowledge Economy, vol. 15, pp. 12191–122152023. doi: 10.1007/s13132-023-01558-5
A. C. Thomaz, “A Short Study Role of Wireless Networks in Smart Agriculture.” Computational Algorithms and Numerical Dimensions, vol. 1, no. 4, pp. 155-158, 2022.
A. A. El-Douh, S. Lu, A. Abdelhafeez, A. M. Ali, A. S. Aziz, “Heart Disease Prediction under Machine Learning and Association Rules under Neutrosophic Environment.” Neutrosophic Systems with Applications, vol. 10, pp. 35-52, 2023.
R. Khaliq, P. Iqbal, S. Bhat, R. Singh, S. Jain, & P. Agarwal, “Generalized Hukuhara derivative approach for Gompertz tumor growth with allee effect under fuzzy environment.” Journal of Fuzzy Extension and Applications, vol. 4, no.4, pp. 246-256, 2023.
A. U. Rahman, “A theoretical context for (θ,β)-convexity and (θ,β)-concavity with hypersoft settings.” Big Data and Computing Visions, vol. 3, no. 4, pp. 196-208, 2023.
F. Smarandache, “Foundation of the SuperHyperSoft Set and the Fuzzy Extension SuperHyperSoft Set: A New Vision.” Neutrosophic Systems with Applications, vol. 11, pp. 48-51, 2023.
F. Smarandache, “Soft set product extended to hypersoft set and indetermsoft set cartesian product extended to indetermhypersoft set.” Journal of Fuzzy Extension and Applications, vol. 3, no. 4, pp. 313-316, 2022.
M. Imran, & N. Ali, “Exploring the Synergy Between Tourism and Economic Development in Pakistan.” Systemic Analytics, vol. 2, no. 1, pp. 48-58, 2024.
H. Taherdoost and M. Madanchian, “Multi-Criteria Decision Making (MCDM) Methods and Concepts,” Encyclopedia, vol. 3, no. 1, pp. 77-87, 2023. doi: 10.3390/encyclopedia3010006
Brauers, M. K. Willem and E. K. Zavadskas, “The MOORA method and its application to privatization in a transition economy,” Control and Cybernetics, vol. 35, pp. 445-469, 2006.
L. Breiman, "Random Forests." Machine Learning, vol. 45, no. 1, pp. 5-32, 2001. doi: 10.1023/A:1010933404324
M. Tsakalerou, D. Efthymiadis, A. Abilez, "An intelligent methodology for the use of multicriteria decision analysis in impact assessment: the case of real-world offshore construction." Scientific Reports, vol. 12, no. 1, pp. 15137, 2022.
Y. Chen, Y. Zhou, F. Shiqi, M. L. Y. Wang and K. Cao, "Crop pattern optimization for the coordination between economy and environment considering hydrological uncertainty," Science of The Total Environment, vol. 809, p. 151152, 2022.
M. Saqlain, P. Kumam, W. Kumam. “Uncertainty and Decision-Making in Crop Economics Using Fuzzy Hypersoft Set with MULTIMOORA Method and Machine Learning.” In: Ngoc Thach, N., Trung, N.D., Ha, D.T., Kreinovich, V. (eds) Partial Identification in Econometrics and Related Topics. Studies in Systems, Decision and Control, vol 531. Springer, Cham.
Q. Sun, J. Ren, and F. Zhao, "Sliding mode control of discrete-time interval type-2 fuzzy Markov jump systems with the preview target signal," Applied Mathematics and Computation, vol. 435, p. 127479, 2022. doi: 10.1016/j.amc.2022.127479
J. Ge and S. Zhang, "Adaptive Inventory Control Based on Fuzzy Neural Network under Uncertain Environment," Complexity, vol. 2020, no. 1, p. 6190936, 2019. doi: 10.1155/2020/6190936
N. Zhang, W. Qi, G. Pang, J. Cheng, and K. Shi, "Observer-based sliding mode control for fuzzy stochastic switching systems with deception attacks," Applied Mathematics and Computation, vol. 427, p. 127153, 2022. doi: 10.1016/j.amc.2022.127153
M. Sarwar, H., and T. Li, "Fuzzy fixed-point results and applications to ordinary fuzzy differential equations in complex valued metric spaces," Hacettepe Journal of Mathematics and Statistics, vol. 48, no. 6, pp. 1712-1728, 2019. doi: 10.15672/HJMS.2018.633
Y. Xia, J. Wang, B. Meng, and X. Chen, "Further results on fuzzy sampled-data stabilization of chaotic nonlinear systems," Applied Mathematics and Computation, vol. 379, p. 125225, 2020. doi: 10.1016/j.amc.2020.125225
M. Gao, L. Zhang, W. Qi, J. Cao, J. Cheng, Y. Kao, Y. Wei, and X. Yan, "SMC for semiMarkov jump T-S fuzzy systems with time delay," Applied Mathematics and Computation, vol. 374, p. 125001, 2020. doi: 10.1016/j.amc.2019.125001
M. Madić, G. Petrović, D. Petković, and P. Janković, "Traditional and Integrated MCDM Approaches for Assessment and Ranking of Laser Cutting Conditions," Spectrum of Mechanical Engineering and Operational Research, vol. 1, no. 1, pp. 250-257, 2024. doi: 10.31181/smeor11202422
M. Saqlain, "Sustainable Hydrogen Production: A Decision-Making Approach Using VIKOR and Intuitionistic Hypersoft Sets," Journal of Intelligent and Management Decision, vol. 2, no. 3, pp. 130-138, 2023. doi: 10.56578/jimd020303
M. Asif, U. Ishtiaq, and I. K. Argyros, "Hamacher Aggregation Operators for Pythagorean Fuzzy Set and its Application in Multi-Attribute Decision-Making Problem," Spectrum of Operational Research, vol. 2, no. 1, pp. 27-40, 2024. doi: 10.31181/sor2120258
H. B. U. Haq, & M. Saqlain, “Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach,”Journal of Industrial Intelligence, vol. 1, no. 3, pp. 136-147, 2023. doi: 10.56578/jii010301
S. Kousar, A. Ansar, N. Kausar, and G. Freen, "Multi-Criteria Decision-Making for Smog Mitigation: A Comprehensive Analysis of Health, Economic, and Ecological Impacts," Spectrum of Decision Making and Applications, vol. 2, no. 1, pp. 53-67, 2024. doi: 10.31181/sdmap2120258
J. Kannan, V. Jayakumar, and M. Pethaperumal, "Advanced Fuzzy-Based Decision-Making: The Linear Diophantine Fuzzy CODAS Method for Logistic Specialist Selection," Spectrum of Operational Research, vol. 2, no. 1, pp. 41-60, 2024. doi: 10.31181/sor2120259
W. Kumam, K. Khammahawong, M. J. Khan, T. Bantaojai and S. Phiangsungnoen, “Fermatean Fuzzy Divergences and Their Applications to Decision-making and Pattern Recognition,” Nonlinear Convex Analysis and Optimization: An International Journal on Numerical, Computation and Applications, vol. 2, no. 1, pp 31–53, 2023. doi: 10.58715/ncao.2023.2.2
M. S. Sezen, H. Isık, & D. Turkoglu, “A New Method for Some Fixed-Point Theorems in Fuzzy Metric Spaces,” Bangmod International Journal of Mathematical and Computational Science, vol. 3, pp. 10–15, 2017.
Published
2025-04-08
How to Cite
SAQLAIN, Muhammad; KUMAM, Poom; KUMAM, Wiyada.
Optimizing Agricultural Decision-Making with Integrated MCDM-MCDA Methods: A Case Study on Crop Economics.
Yugoslav Journal of Operations Research, [S.l.], apr. 2025.
ISSN 2334-6043.
Available at: <https://yujor.fon.bg.ac.rs/index.php/yujor/article/view/1334>. Date accessed: 14 apr. 2025.
doi: https://doi.org/10.2298/YJOR240915008S.
Issue
Section
Research Articles
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
