<![CDATA[This research proposes a novel Probabilistic Decision Model (PDM) designed to address the challenges of optimization in highly complex systems characterized by high-dimensional states, nonlinear interactions, and deep uncertainty. Traditional deterministic, heuristic, and deep learning-based methods often fail to provide reliable decisions under such conditions due to their limited scalability, lack of uncertainty quantification, or inability to guarantee constraint satisfaction. The proposed model integrates probabilistic constraints, expectation-based objective functions, and adaptive AI-driven scenario generation to deliver a robust and flexible optimization framework. A rigorous mathematical formulation is presented, including probability space definitions, risk measures, and feasible neighborhood rules. Validation through numerical simulations demonstrates that the model maintains high feasibility, reduces worst-case risks, and remains stable even under extreme uncertainty. Case studies in smart grid optimization, logistics routing, and manufacturing scheduling further highlight significant performance improvements over classical stochastic optimization, MDP/POMDP models, and deep reinforcement learning without probabilistic modeling. The results confirm the model’s strong scalability, enhanced uncertainty modeling, and practical relevance for real-world industrial environments. This research contributes a hybrid probabilistic-AI framework that advances the reliability, resilience, and intelligence of decision-making in modern complex systems, while opening pathways for future exploration in multi-agent coordination, automated parameter tuning, and real-time adaptive optimization.]]>
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