<![CDATA[The increasing pressure to integrate sustainability into industrial practices has led to the need for more efficient systems optimization models that address resource constraints. Traditional optimization models in industrial engineering have focused predominantly on maximizing efficiency and minimizing costs, often overlooking the long-term environmental and social impacts. This research explores the intersection of sustainable development and systems optimization under resource limitations, aiming to develop a comprehensive framework that balances economic, environmental, and social factors in industrial processes. The study employs a mixed-methods approach, combining literature review, case studies from various industrial sectors, and mathematical optimization models. The results demonstrate that the integration of resource constraints into industrial systems significantly improves both operational performance and sustainability outcomes. Industries, particularly in manufacturing and logistics, showed considerable improvements in production efficiency and reductions in energy consumption and material waste. The research concludes that resource-constrained optimization models can lead to more sustainable industrial practices without compromising economic efficiency. The findings provide a valuable contribution to the field of industrial engineering, offering a framework that can be applied across diverse sectors seeking to optimize their systems within the boundaries of available resources. Future studies should extend these models to include more complex industrial sectors and explore long-term sustainability impacts.]]>
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