Theory of AI-driven scheduling (TAIS): a service-oriented scheduling framework by integrating theory of constraints and AI

This paper introduces the Theory of AI-driven scheduling (TAIS), an innovative framework designed to revolutionise service-oriented scheduling (In the context of this paper, 'service-oriented' refers to the design and execution of scheduling processes that are specifically tailored to meet the dynamic and varied needs of service-manufacturing industries. This approach emphasises adaptability, customer-centric scheduling, and the efficient allocation of resources to enhance product and service delivery.) by integrating the theory of constraints (TOC) (see APPENDIX I) with cutting-edge Artificial Intelligence (AI) technologies. TAIS extends the traditional five steps of TOC by introducing three additional layers to enhance adaptability, efficiency, and applicability in service-oriented environments. TAIS incorporates continuous monitoring and adjustment mechanisms across all five TOC steps to improve adaptability, ensuring real-time responsiveness to dynamic scheduling challenges. To enhance efficiency, TAIS integrates lifecycle management and governance into the core framework, which helps in the efficient allocation of resources throughout the scheduling process. Lastly, TAIS includes robust security and compliance measures to bolster applicability. By leveraging AI, TAIS offers a predictive, flexible, and scalable solution capable of addressing the complexities and rapid changes inherent in dynamic scheduling tasks and providing a significant leap forward in scheduling and operations management.