Artificial Intelligence-Enabled Resilient Scheduling: A Systematic Review and Research Roadmap for Digital Twin and Machine Learning in Disruption-Aware Operations

Chukwudi Emeka Udu¹, Charles Chikwendu Okpala²

¹ Researcher, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Anambra State, Nigeria.

² Professor, Industrial/Production Engineering Department, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. 

Abstract

The accelerating frequency of climate-induced disruptions, geopolitical volatility, and cyber-physical risks has exposed the structural fragility of efficiency-driven scheduling systems across manufacturing, logistics, healthcare, and energy operations. At the same time, global decarbonization imperatives demand measurable reductions in energy consumption and carbon emissions from operational processes. This article presents a systematic review and meta-analytic synthesis of AI-enabled resilient scheduling, with particular emphasis on the integration of digital twin technologies and machine learning for disruption-aware and sustainability-oriented operations. Following a PRISMA-guided methodology, 1,284 records published between 2010 and 2025 were screened, resulting in 148 peer-reviewed studies for qualitative synthesis and 63 studies for quantitative meta-analysis. The results indicate that AI-driven scheduling architectures achieve an average 28% reduction in disruption recovery time, 16% operational cost savings, and 8–15% energy consumption reductions relative to conventional deterministic models. Carbon emissions reductions across sectors range from 6% to 14%, with the largest gains observed in energy-intensive manufacturing and smart grid applications. Building on these findings, the article proposes the Adaptive Twin-Reinforcement Scheduling (ATRS) framework, a novel multi-layer architecture that integrates real-time data fusion, predictive intelligence, digital twin simulation, and multi-objective reinforcement learning with embedded carbon-aware reward structures. Simulation benchmarking across 10,000 disruption scenarios demonstrates a 34% improvement in composite resilience performance and a 12.7% average reduction in carbon intensity without compromising service levels. By positioning sustainability metrics as endogenous components of adaptive scheduling policies, this study advances a methodological shift from reactive efficiency optimization towards climate-aligned, resilience-centered operational intelligence. The article concludes with a multidisciplinary research roadmap to 2035, and outlines theoretical, computational, and governance priorities that are necessary for scalable deployment of AI-enabled resilient scheduling systems that are capable of supporting net-zero and disruption-resilient industrial ecosystems.       

Keywords: AI-enabled scheduling, Disruption-aware operations, Digital twins, Reinforcement learning, Sustainability optimization, Resilient supply chains, Carbon-aware decision-making  

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