Ali, AtifSaid, Raed A.Rizwan, Hafiz Muhammad AmirShehzad, KhurramNaz, Imran2022-05-222022-05-22© 20222022Ali, A., Said, R. A., Rizwan, H. M. A., Shehzad, K., & Naz, I. (2022). Application of computational intelligence and machine learning to conventional operational research methods. 2022 International Conference on Business Analytics for Technology and Security (ICBATS). https://doi.org/10.1109/ICBATS54253.2022.9759033978-166540920-9https://doi.org/10.1109/ICBATS54253.2022.9759033http://hdl.handle.net/20.500.12519/648This conference paper is not available at CUD collection. The version of scholarly record of this paper is published in 2022 International Conference on Business Analytics for Technology and Security (ICBATS) (2022), available online at: https://doi.org/10.1109/ICBATS54253.2022.9759033Machine learning and computational intelligence are two methods for achieving this (CI); traditional operational research methods are combined with machine learning-based computational techniques (OR). Students can handle complex decision-making problems thanks to the synergy between those methods and techniques. This research's primary goal is to present and demonstrate potential connections amid the two computational arenas. Using applications, we show how machine learning techniques like fuzzy logic, neural networks and reinforcement learning can be combined to provide a simpler solution to more complex problems than traditional OR methods., which is a research contribution in and of itself. © 2022 IEEE.en-USPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.Hybrid ModelsMachine LearningOperational ResearchOptimizationConference PaperCopyright : © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.