<![CDATA[The growing demand for sustainable and energy-efficient operations has intensified interest in optimization methods that reduce energy consumption without compromising productivity. Hybrid Genetic Algorithms (HGAs) have shown promise in tackling complex scheduling problems characterized by nonlinear, multi-objective, and energy-constrained conditions. Despite numerous studies, a clear gap remains: existing reviews often focus narrowly on manufacturing or provide descriptive summaries without quantifying performance gains or highlighting methodological trends. This paper addresses this deficiency by systematically reviewing 47 peer-reviewed studies published between 2010 and 2025, encompassing 34 manufacturing-oriented models (e.g., flow shop, flexible flow shop, job shop, and parallel machine) and 13 cross-domain applications where HGA principles have been adapted (e.g., EV charging, smart grids, and building energy systems). Using a PRISMA-inspired protocol, studies are analyzed along five dimensions: scheduling environment, hybridization mechanism, energy modeling approach, performance indicators, and implementation maturity. Quantitative synthesis indicates that heuristic-assisted and local search-based hybridizations dominate, while integrations with reinforcement learning and mathematical programming offer significant improvements in adaptivity and solution quality. Total Energy Consumption (TEC) and peak power minimization are the primary objectives, yet dynamic or real-time energy feedback remains underexplored. Building on this analysis, the paper proposes a conceptual framework that unifies HGA structures across manufacturing and smart energy systems, emphasizing methodological consistency, adaptive control, and sustainability-driven optimization. The review not only consolidates performance trends but also delineates clear research gaps, providing actionable directions for future work on hybrid metaheuristics in environmentally sustainable scheduling.]]>
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