Multi-Layered Decision Matrices in Self-Adjusting Operational Frameworks with Dynamic Constraints

Abstract

Operational contexts are becoming more complex in nature, and the control systems should be able to adapt to the constantly changing constraints, availability of resources, and complex system behaviours. The proposed architecture has an operation structure that is self-adjusting and multi-layered decision matrices that execute tri-level strategic, tactical, and operational decisions autonomously in a changing constraint. Feedback loops with constraint relaxation and matrix-based decision propagation also promote the adaptability and decentralized responsiveness of the lattice structure. The paper will begin with a formal model of the matrix structure and dynamics of constraints, and then will move on to simulations and real-world case analyses of the accuracy, latency, throughput, and robustness of the framework. The results show better satisfaction of the operational constraints, less latency, and constant system behavior under asynchronous pattern conditions. The additional comparison by the decentralized, rules-based control helped to point out superior responsiveness and scalability at different loads. This paper introduced intelligent robust control systems, intelligent manufacturing, intelligent energy systems, and intelligent supply chains, and also established the field of future investigation into explainable heuristics, digital twins, and autonomous development of the control matrices.