AI-Assisted Resource Scheduling for Sustainable and Resilient Engineering Operations
Keywords:
AI-assisted scheduling, resource optimization, sustainability, resilience engineering, governance, infrastructureAbstract
The increasing complexity of large-scale engineering operations, coupled with mounting pressures for environmental sustainability and operational resilience, has driven the adoption of artificial intelligence (AI) in resource scheduling. This paper examines AI-assisted scheduling as a systemic intervention at the intersection of computational optimization, lifecycle sustainability, and resilience engineering. We develop a multi-layered analysis that spans foundational system architectures, sustainability frameworks, resilience strategies, governance mechanisms, policy implications, and deployment infrastructures. The discussion reveals that realizing the full potential of AI scheduling demands more than algorithmic sophistication; it requires careful reconciliation of structural trade-offs between centralized efficiency and decentralized adaptability, predictive optimization and real-time reconfiguration, and energy-aware allocation and stringent latency guarantees. Through extended conceptual analysis and cross-domain comparison, we argue that sustainable and resilient operations emerge from the deliberate integration of AI schedulers within governance frameworks that institutionalize fairness, accountability, and lifecycle thinking. The paper contributes an interdisciplinary synthesis that positions AI-assisted scheduling as a key architectural lever for transforming engineering infrastructures into adaptive, low-carbon, and socially responsive systems.
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