Multi-Criteria Decision-Making (MCDM) plays a critical role in identifying optimal solutions in complex environments where multiple, often conflicting, criteria must be considered. This paper presents a hybrid Artificial Intelligence (AI) framework that integrates a Genetic Algorithm (GA) with the Multi-Objective Optimization by Ratio Analysis (MOORA) method. The GA provides global search and optimization capabilities for determining criterion weights, while MOORA offers a computationally simple, robust, and rank-stable approach for evaluating alternatives. The proposed methodology consists of three stages: 1) identifying the decision alternatives and relevant evaluation criteria, 2) determining the criteria weights using a GA, and 3) ranking the alternatives using the MOORA method. The effectiveness of the hybrid GA–MOORA approach is validated through a comparative case study based on the dataset from [11] to determine the optimal weighting factors. Results demonstrate a strong agreement between MOORA and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both methods identify Alternative 1 (q = 0.9) as the least favorable option (ranked 5th), while the mid-range alternatives (Alternatives 4 and 5) exhibit similar rankings. The proposed GA-MOORA model identifies Alternative 3 (q = 0.5) as having the highest net utility, with Alternative 2 (q = 0.3) performing comparably. This close performance provides decision-makers with flexible, reliable options for final selection.
Keywords
Artificial IntelligentMCDMGenetic AlgorithmMOORA.
References
Z. Chourabi, F. Khedher, A. Babay, and M. Cheikhrouhou, “Multi-criteria decision making in workforce choice using AHP, WSM and WPM,” J. Textile Inst., vol. 110, no. 7, pp. 1092–1101, 2019.
R. Kumar, R. Dubey, S. Singh, S. Singh, C. Prakash, Y. Nirsanametla, and R. Chudy, “Multiple-criteria decision-making and sensitivity analysis for selection of materials for knee implant femoral component,” Materials, vol. 14, no. 8, 2084, 2021.
A. Kolios, V. Mytilinou, E. Lozano-Minguez, and K. Salonitis, “A comparative study of multiple-criteria decision-making methods under stochastic inputs,” Energies, vol. 9, no. 7, 566, 2016.
Budiharjo, Windarto, A. Perdana, and M. Abulwafa, “Comparison of weighted sum model and multi attribute decision making weighted product methods in selecting the best elementary school in Indonesia,” Int. J. Softw. Eng. Appl., vol. 11, no. 4, pp. 69–90, 2017.
A. M. AlAli, A. Salih, and A. Hassaballa, “Geospatial-based analytical hierarchy process (AHP) and weighted product model (WPM) techniques for mapping and assessing flood susceptibility in the Wadi Hanifah Drainage Basin, Riyadh Region, Saudi Arabia,” Water, vol. 15, no. 10, 2023.
Y. Han, Z. Wang, X. Lu, and B. Hu, “Application of AHP to road selection,” ISPRS Int. J. Geo-Inf., vol. 9, no. 2, 86, 2020.
E. Jayamani, D. S. Perera, K. H. Soon, and M. Bakri, “Application of analytic hierarchy process (AHP) in the analysis of the fuel efficiency in the automobile industry with the utilization of natural fiber polymer composites,” in IOP Conf. Ser.: Mater. Sci. Eng., vol. 191, no. 1, 012004.
H. Yaseen and H. Naji, “Applying analytical network process to the selection of financing method for abandoned construction projects in Iraq,” Diyala J. Eng. Sci., pp. 62–70, 2021.
L. Zarei, N. Moradi, F. Peiravian, and G. Mehralian, “An application of analytic network process model in supporting decision making to address pharmaceutical shortage,” BMC Health Serv. Res., vol. 20, no. 1, 626, 2020.
S. Malallah and Z. H. Ali, “TOPSIS with multiple linear regression for multi-document text summarization,” Iraqi J. Sci., pp. 1298–1307, 2017.
D. Dhayef, S. Al-Zubaidi, L. Al-Kindi, and E. Tirkolaee, “Cell formation and layout design using genetic algorithm and TOPSIS: A case study of Hydraulic Industries State Company,” PLOS One, vol. 19, no. 1, e0296133, 2024.
S. Shihab and E. Mubarak, “Multi-objective optimization of wire EDM parameters by applying MOORA technique,” Iraqi J. Mech. Mater. Eng., vol. 17, no. 2, pp. 354–364, 2017.
Y. Ic, “A multi-objective credit evaluation model using MOORA method and goal programming,” Arab. J. Sci. Eng., vol. 45, no. 3, pp. 2035–2048, 2020.
A. Hussein, L. Abbas, and A. K. Hameed, “Effect of carburization parameters on hardness of carburized steel using MOORA approach,” Al-Khwarizmi Eng. J., vol. 14, no. 3, pp. 92–99, 2018.
Y. T. İç and S. Yıldırım, “MOORA-based Taguchi optimisation for improving product or process quality,” Int. J. Prod. Res., vol. 51, no. 11, pp. 3321–3341, 2013.
A. Abraheim, A. Ali, and R. Al-Salih, “Flow optimization in dynamic networks on time scales,” in J. Phys.: Conf. Ser., vol. 1804, no. 1, 012025, 2021.
R. Al-Salih, “Dynamic network flows in quantum calculus,” J. Anal. Appl., vol. 18, no. 1, pp. 53–66, 2020.
A. Bhattacharyya and S. Chakraborty, “A DEA-TOPSIS-based approach for performance evaluation of Indian technical institutes,” Decis. Sci. Lett., vol. 3, no. 3, pp. 397–410, 2014.
S. Rakhshan, “Efficiency ranking of decision making units in data envelopment analysis by using TOPSIS-DEA method,” J. Oper. Res. Soc., vol. 68, no. 8, pp. 906–918, 2017.
C. Peng, D. Feng, and S. Guo, “Material selection in green design: A method combining DEA and TOPSIS,” Sustainability, vol. 13, no. 10, 5497, 2021.
S. S. Ray and S. Misra, “Genetic algorithm for assigning weights to gene expressions using functional annotations,” Comput. Biol. Med., vol. 104, pp. 149–162, 2019.
M. Wasid, R. Ali, and S. Shahab, “Adaptive genetic algorithm for user preference discovery in multi-criteria recommender systems,” Heliyon, vol. 9, no. 7, 2023.
K. Sundararajan and K. Srinivasan, “A synergistic optimization algorithm with attribute and instance weighting approach for effective drought prediction in Tamil Nadu,” Sustainability, vol. 16, no. 7, 2936, 2024.
·
This work is licensed under a
Creative Commons Attribution-ShareAlike 4.0 International License
