The increasing complexity of modern distribution systems has created significant challenges in achieving robust planning, efficient resource allocation, and adaptive decision-making under uncertain and dynamic operating conditions. Traditional optimization-based approaches often struggle to address rapidly changing demand patterns, decentralized information environments, communication constraints, and adversarial disruptions. This research proposes an experience-driven computational strategy that integrates distributed learning, federated intelligence, and reinforcement-based adaptive optimization to enhance robustness and operational efficiency in distribution systems. The proposed framework leverages accumulated operational experience as a computational asset, enabling systems to continuously improve planning decisions through data-driven learning while maintaining scalability across distributed environments.

The research develops a conceptual architecture combining distributed statistical learning mechanisms, privacy-preserving collaborative intelligence, and reinforcement-driven forecasting capabilities. The theoretical foundation is derived from distributed machine learning and decentralized optimization principles, where multiple system entities collaboratively improve decision quality without requiring complete centralization of data. Federated learning strategies provide the foundation for efficient knowledge exchange among distributed nodes, while reinforcement learning mechanisms enable adaptive resource allocation based on changing environmental conditions. Existing research on Byzantine-resilient distributed learning, communication-efficient federated learning, and edge-based adaptive control provides important theoretical support for developing robust computational strategies.

The proposed methodology introduces an experience-driven decision framework consisting of four interconnected layers: distributed data acquisition, collaborative intelligence generation, adaptive resource optimization, and continuous feedback-based improvement. The framework addresses critical challenges including communication overhead, resource limitations, uncertainty management, and system resilience. Furthermore, reinforcement learning-based forecasting approaches demonstrate significant potential in improving prediction accuracy and supply chain optimization by learning complex operational patterns from historical and real-time information (Viswanathan et al., 2025).

Experience-driven computing, distribution systems, resource allocation, federated learning

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