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

محورهای موضوعی : فصلنامه ریاضی

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

1 - Department of Industrial Management , Isfahan Branch (Khorasgan), Islamic Azad University, Isfahan, Iran.
2 - Department of Science, Isfahan Branch, Islamic Azad University, Isfahan, Iran.
3 - Al-Karkh University of Science, Baghdad, Iraq.
4 - Department of Management, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran.

تاریخ دریافت : 1403/01/12 تاریخ پذیرش : 1403/04/05 تاریخ انتشار : 1403/07/08

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

چکیده مقاله :

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.‎

چکیده انگلیسی:

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.‎

منابع و مأخذ:


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