Integrating Multi-Criteria Decision Analysis with Deep Reinforcement Learning: A Novel Framework for Intelligent Decision-Making in Iraqi Industries

Subject Areas : International Journal of Mathematical Modelling & Computations

Ali Ghani Nori Alsaedi ¹ , Mohammad Jalali Varnamkhasti ² , Husam Jasim Mohammed ³ , Mojtaba Aghajani ⁴

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Received: 2024-03-31 Accepted : 2024-06-25 Published : 2024-09-29

Keywords: Artificial Intelligence, Multi-Criteria Decision Analysis, Deep Reinforcement Learning, Intelligent Decision-Making, Iraqi Industries.,

Abstract :

This study proposes a novel framework that integrates Multi-Criteria Decision Analysis (MCDA) with Deep Reinforcement Learning (DRL) to enhance decision-making processes, particularly in the context of the Iraqi oil industry. As this sector faces rapid changes and increasing competition, the demand for efficient and intelligent decision-making has become critical. The proposed framework specifically targets supplier selection in the procurement of raw materials, addressing the complexities involved in evaluating potential suppliers. Key criteria such as cost, quality, delivery time, and sustainability are considered, ensuring a comprehensive assessment of suppliers. By leveraging the structured decision-making approach provided by MCDA, the framework allows for systematic evaluation against these criteria. Simultaneously, the adaptive learning capabilities of DRL facilitate the continuous improvement of supplier selection strategies over time. This dynamic model not only enhances the accuracy of decision-making but also allows organizations to respond swiftly to evolving market conditions and supplier performance. Ultimately, this integrated approach aims to optimize procurement processes, reduce risks, and drive better outcomes in the oil industry, contributing to more sustainable and efficient operations. Through this innovative framework, the study seeks to provide valuable insights and practical tools for decision-makers in the sector.‎

References:


[1] T. M. Alabi, E. I. Aghimien, F. D. Agbajor, Z. Yang, L. Lu, A. R. Adeoye and B. Gopaluni, A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems, Renewable Energy, 194 (2022) 822-849.
[2] M. A. Alias, S. Z. M. Hashim and S. Samsudin, Multi criteria decision making and its applications: a literature review, Jurnal Teknologi Maklumat, 20(2) (2008) 129-152.
[3] M. Andronie, G. Lăzăroiu, M. Iatagan, C. Uță, R. Ștefănescu and M. Cocoșatu, Artificial intelligence-based decision-making algorithms, internet of things sensing networks, and deep learning-assisted smart process management in cyber-physical production systems. Electronics, 10(20) (2021) 2497.
[4] S. Angamuthu, and P. Trojovský, Integrating multi-criteria decision-making with hybrid deep learning for sentiment analysis in recommender systems, PeerJ Computer Science, 9 (2023) e1497.
[5] K. Arulkumaran, M. P. Deisenroth, M. Brundage and A. A. Bharath, A brief survey of deep reinforcement learning, arXiv preprint (2017). 1708.05866.
[6] P. P. Balasubramani, V. S. Chakravarthy, B. Ravindran and A. A. Moustafa, An extended reinforcement learning model of basal ganglia to understand the contributions of serotonin and dopamine in risk-based decision making, reward prediction, and punishment learning. Frontiers in computational neuroscience, 8 (2014) 47.
[7] V. Belton and T. Stewart, Multiple criteria decision analysis: an integrated approach, Springer Science & Business Media (2012).
[8] R. Bogacz and T. Larsen, Integration of reinforcement learning and optimal decision-making theories of the basal ganglia, Neural computation, 23(4) (2011) 817-851.
[9] V. Borysenko, G. V. Kondratenko, I. V. Sidenko and Y. P. Kondratenko, Intelligent forecasting in multi-criteria decision-making. In CMIS, (2020) 966-979.
[10] Q. Cappart, E. Goutierre, D. Bergman and L. M. Rousseau, improving optimization bounds using machine learning: Decision diagrams meet deep reinforcement learning. In Proceedings of the AAAI Conference on Artificial Intelligence 33 (1) (2019)1443-1451.
[11] J. Černevičienė and A. Kabašinskas, Review of multi-criteria decision-making methods in finance using explainable artificial intelligence, Frontiers in artificial intelligence, 5 (2022) 827584.
[12] X. Chen, G. Qu, Y. Tang, S. Low and N. Li, Reinforcement learning for decision-making and control in power systems: Tutorial, review, and vision. arXiv preprint arXiv:2102.01168 (2021).
[13] X. Chu, B. Sun, X. Chu, L. Wang, K. Bao and N. Chen, Multi-modal and multi-criteria conflict analysis model based on deep learning and dominance-based rough sets: Application to clinical non-parallel decision problems. Information Fusion, (2024) 102636.
[14] T. Cui, X. Yang, F. Jia, J. Jin, Y. Ye and R. Bai, Mobile robot sequential decision making using a deep reinforcement learning hyper-heuristic approach. Expert Systems with Applications, (2024) 124959.
[15] C.N. Dang, M.N. Moreno-García, F. De la Prieta, Hybrid deep learning models for sentiment analysis. Complexity (2021) 9986920.
[16] P. Dayan and N. D. Daw, Decision theory, reinforcement learning, and the brain, Cognitive, Affective, & Behavioral Neuroscience, 8(4) (2008) 429-453.
[17] H. Dong, H. Dong, Z. Ding, S. Zhang and T. Chang, Deep Reinforcement Learning. Singapore: Springer Singapore (2020).
[18] W. Edwards, The theory of decision making, Psychological bulletin, 51(4) (1954)380.
[19] M. Fang, Y. Li and T. Cohn, Learning how to active learn: A deep reinforcement learning approach. arXiv preprint (2017) 1708.02383.
[20] L. Fontanesi, S. Gluth, M. S. Spektor and J. Rieskamp, A reinforcement learning diffusion decision model for value-based decisions. Psychonomic bulletin & review, 26(4) (2019) 1099-1121.
[21] V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare and J. Pineau, An introduction to deep reinforcement learning, Foundations and Trends® in Machine Learning, 11(4) (2018) 219-354.
[22] M. J. Frank and E. D. Claus, Anatomy of a decision: striato-orbitofrontal interactions in reinforcement learning, decision making, and reversal, psychological review, 113(2) (2006). 300.
[23] Y. Fu, C. Li, F. R. Yu, T. H. Luan and Y. Zhang, A decision-making strategy for vehicle autonomous braking in emergency via deep reinforcement learning. IEEE transactions on vehicular technology, 69(6) (2020) 5876-5888.
[24] K. Guo, R. Liu, G. Duan and J. Liu, A Deep reinforcement learning method with multiple starting nodes for dynamic process planning decision making. Computers & Industrial Engineering, 194 (2024) 110359.
[25] M. M. Hasan, An Intelligent Decision-making Scheme in a Dynamic Multi-objective Environment using Deep Reinforcement Learning (Doctoral dissertation, Anglia Ruskin Research Online) (2023).
[26] X. Han, J. Wang, J. Xue and Q. Zhang, Intelligent decision-making for 3-dimensional dynamic obstacle avoidance of UAV based on deep reinforcement learning. In 2019 11th International conference on wireless communications and signal processing (WCSP) (2019) 1-6.
[27] A. Harris, T. Teil and H., Spacecraft decision-making autonomy using deep reinforcement learning. In 29th AAS/AIAA space flight mechanics meeting, Hawaii (2019) 1-19.
[28] Z. He, K. P. Tran, S. Thomassey, X. Zeng, J. Xu, and C. Yi, A deep reinforcement learning based multi-criteria decision support system for optimizing textile chemical process. Computers in Industry, 125 (2021) 103373.
[29] C. J. Hoel, K. Driggs-Campbell, K. Wolff, L. Laine and M. J. Kochenderfer, combining planning and deep reinforcement learning in tactical decision making for autonomous driving, IEEE transactions on intelligent vehicles, 5(2) (2019) 294-305.
[30] C. J. Hoel, K. Wolff, and L. Laine, Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation. In 2020 IEEE intelligent vehicles symposium 4 (2020) 1563-1569.
[31] M. Hussain, M. Tayyab, K. Ullah, S. Ullah, Z. U. Rahman, J. Zhang and B. Al-Shaibah, Development of a new integrated flood resilience model using machine learning with GIS-based multi-criteria decision analysis. Urban Climate, 50 (2023) 101589.
[32] F. Hutter, L. Kotthoff and J. Vanschoren, Automated machine learning: methods, systems, challenges (2019) 219, Springer Nature.
[33] T. L. Jassim, The influence of oil prices, licensing and production on the economic development: An empirical investigation of Iraq economy, AgBioForum, 23(1) (2021)1-11.
[34] G. Jeon, M. Anisetti, E. Damiani, B. Kantarci, Artificial intelligence in deep learning algorithms for multimedia analysis, Multimedia Tools and Applications 79(45) (2020) 34129-34139
[35] W. Jiang, Y. Ren and Y. Wang, Improving anti-jamming decision-making strategies for cognitive radar via multi-agent deep reinforcement learning. Digital Signal Processing, 135 (2023) 103952.
[36] M. Lapeyrolerie, M. S. Chapman, K. E. Norman, and C. Boettiger, Deep reinforcement learning for conservation decisions, Methods in Ecology and Evolution, 13(11) (2022) 2649-2662.
[37] J. Liao, T. Liu, X. Tang, X. Mu, B. Huang, D. Cao, Decision-making strategy on highway for autonomous vehicles using deep reinforcement learning. IEEE Access, 8 (2020)177804-177814.
[38] G. Li, S. Li, S. Li, Y. Qin, D. Cao, X. Qu and B. Cheng, Deep reinforcement learning enabled decision-making for autonomous driving at intersections. Automotive Innovation, 3(2020) 374-385.
[39] S. E. Li, Deep reinforcement learning. In Reinforcement learning for sequential decision and optimal control (2023) 365-402. Singapore: Springer Nature Singapore.
[40] Y. Li, Deep reinforcement learning: An overview, arXiv preprint arXiv:1701.07274 (2017).
[41] Y. Liu, M. Yang and Z. Guo, Reinforcement learning based optimal decision making towards product lifecycle sustainability, International Journal of Computer Integrated Manufacturing, 35(11) (2022) 1269-1296.
[42] K. Lv, X. Pei, C. Chen, and J. Xu, A safe and efficient lane change decision-making strategy of autonomous driving based on deep reinforcement learning. Mathematics, 10(9) (2022) 1551.
[43] H. Mao, M. Alizadeh, I. Menache and Kandula, S. Resource management with deep reinforcement learning. In Proceedings of the 15th ACM workshop on hot topics in networks (2016) 50-56.
[44] A. Mardani, E. K. Zavadskas, Z. Khalifah, N. Zakuan, A. Jusoh, K. M. Nor and M. Khoshnoudi, A review of multi-criteria decision-making applications to solve energy management problems: Two decades from 1995 to 2015. Renewable and Sustainable Energy Reviews, 71 (2017) 216-256.
[45] K. Martyn, and M. Kadziński, Deep preference learning for multiple criteria decision analysis. European Journal of Operational Research, 305(2) (2023) 781-805.
[46] K. Moghaddasi and M. Masdari, Blockchain-driven optimization of IoT in mobile edge computing environment with deep reinforcement learning and multi-criteria decision-making techniques. Cluster Computing, (2023) 1-29.
[47] A. Mohammadifar, H. Gholami and S. Golzari, Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk, Journal of Environmental Management, 345 (2023) 118838.
[48] M. Mukadam, A. Cosgun, A. Nakhaei and K. Fujimura, Tactical decision making for lane changing with deep reinforcement learning (2017).
[49] A. Najafi, A. Nemati, M. Ashrafzadeh and S. H. Zolfani, Multiple-criteria decision making, feature selection, and deep learning: A golden triangle for heart disease identification, Engineering Applications of Artificial Intelligence, 125 (2023) 106662.
[50] T. T. Nguyen, N. D. Nguyen, P. Vamplew, S. Nahavandi, R. Dazeley and C. P. Lim, A multi-objective deep reinforcement learning framework. Engineering Applications of Artificial Intelligence, 96 (2020) 103915.
[51] S. L. V. Papineni, S. Yarlagadda, H. Akkineni and A. M. Reddy, Big data analytics applying the fusion approach of multicriteria decision making with deep learning algorithms. arXiv preprint arXiv:2102.02637 (2021).
[52] Z. Pei, A. M. Rojas-Arevalo, F. J. de Haan, N. Lipovetzky and E. A. Moallemi, Reinforcement learning for decision-making under deep uncertainty. Journal of Environmental Management, 359 (2024) 120968.
[53] J. C. Peterson, D. D. Bourgin, M. Agrawal, D. Reichman and T. L. Griffiths, Using large-scale experiments and machine learning to discover theories of human decision-making. Science, 372(6547) (2021). 1209-1214.
[54] B. T. Pham, C. Luu, D. Van Dao, T. Van Phong, H. D. Nguyen, H. Van Le,, ... and I. Prakash, Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-based systems, 219 (2021) 106899.
[55] G. Phillips-Wren and N. Ichalkaranje, Intelligent decision making: An AI-based approach 97 (2008), Springer Science & Business Media.
[56] L. Rew, Intuition in Decision-Making. Image: Journal of Nursing Scholarship 20(3) (1988) 150-154.
[57] J. Russell, Stuart and Peter Norvig, Artificial intelligence: a modern approach. Pearson, (2016).
[58] S. R. Shikhteymour, M. Borji, M. Bagheri-Gavkosh, E. Azimi and T. W. Collins, A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods. Applied geography, 158 (2023) 103035.
[59] Y. R. Shrestha, V. Krishna, G. Von Krogh, Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges. Journal of Business Research, 123 (2021) 588-603.
[60] K. Van Moffaert, Multi-criteria reinforcement learning for sequential decision making problems (2016). (Doctoral dissertation, Ph. D. thesis, Vrije Universiteit Brussel).
[61] N. N. Vo, X. He, S. Liu and G. Xu, Deep learning for decision making and the optimization of socially responsible investments and portfolio. Decision Support Systems, 124 (2019). 113097.
[62] X. Wang, S. Wang, X. Liang, D. Zhao, J. Huang... and Q. Miao, Deep reinforcement learning: A survey. IEEE Transactions on Neural Networks and Learning Systems, 35(4) (2022)5064-5078.
[63] J. Xu, F. Huang, D. Wu, Y. Cui, Z. Yan, and K. Zhang Deep reinforcement learning based multi-AUVs cooperative decision-making for attack–defense confrontation missions. Ocean Engineering, 239 (2021) 109794.
[64] S. Yang, Z. Xu and J. Wang, Intelligent decision-making of scheduling for dynamic permutation flowshop via deep reinforcement learning. Sensors, 21(3) (2021) 1019.
[65] M. Yang, S. Nazir, Q. Xu and S. Ali, Deep Learning Algorithms and Multicriteria Decision‐Making Used in Big Data: A Systematic Literature Review. Complexity, 2020(1) (2020) 2836064.
[66] F. Yang, D. Lyu, B. Liu and S. Gustafson, Peorl: Integrating symbolic planning and hierarchical reinforcement learning for robust decision-making. arXiv preprint arXiv:1804.07779 (2018).
[67] Y. Ye, X. Zhang, and J. Sun, Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment. Transportation Research Part C: Emerging Technologies, 107 (2019) 155-170.
[68] M. Yuan, J. Shan and K. Mi, Deep reinforcement learning based game-theoretic decision-making for autonomous vehicles. IEEE Robotics and Automation Letters, 7(2) (2021) 818-825.
[69] C. Zhang, G. Zhou, J. Li, T. Qin, K. Ding, and F. Chang, KAiPP: An interaction recommendation approach for knowledge aided intelligent process planning with reinforcement learning. Knowledge-Based Systems, 258 (2022) 110009.
[70] C. Zhu, An adaptive agent decision model based on deep reinforcement learning and autonomous learning. J. Logist. Inform. Serv. Sci, 10 (2023) 107-118.
[71] C. Zuheros, E. Martínez-Cámara, E. Herrera-Viedma, F. Herrera, Sentiment analysis based multi-person multi-criteria decision-making methodology using natural language processing and deep learning for smarter decision aid. Case study of restaurant choice using TripAdvisor reviews. Information Fusion, 68 (2021) 22-36.