A Q4 Production Optimization Project built on *A Multi-Objective Optimization Model Using Improved NSGA-II for Optimizing Metal Mines Production Process* aims to push the mine’s existing system to its maximum potential in the final quarter of the year. This approach focuses on short-term but high-impact improvements that can increase production efficiency, profit, and resource utilization using real operational data. The study from the **Huogeqi Copper Mine** (published in *Processes, MDPI*) provides a solid framework for such optimization. It defines three critical decision variables: **geological cut-off grade, minimum industrial grade**, and **loss ratio**—factors that directly influence economic benefit and resource efficiency. The improved **NSGA-II (Non-dominated Sorting Genetic Algorithm II)** algorithm is used to process large datasets and find the best trade-offs between two main objectives: **maximizing profit** and **improving resource utilization**. Unlike traditional single-objective methods, NSGA-II creates a **Pareto front**, a set of optimal solutions that show how one objective can improve without significantly worsening the other. The study revealed that small, precise adjustments in mine parameters could increase profit by **about 2.99%** and resource utilization by **around 2.64%** compared to the mine’s actual performance. This proves that optimization during Q4 doesn’t require major capital investment—just smarter decision-making. In practical Q4 operations, engineers and managers can apply the same method. They start by gathering current mine data—production rates, grades, energy use, and equipment performance. Then, using the optimization model, they test various scenarios. For example, slightly **lowering the cut-off grade** can bring more ore into the mill if processing costs are under control. Adjusting the **industrial grade** threshold ensures the plant processes material at the best efficiency, while optimizing the **loss ratio** helps reduce wasted ore in extraction or processing. The NSGA-II algorithm helps compare all these possibilities and rank them based on performance impact. In **underground mining**, this approach can optimize stope sequencing, ventilation distribution, and mucking cycles to achieve smoother operations and higher tonnage. In **open-pit mining**, it can optimize haul road gradients, bench design, and truck dispatching to cut down idle time and energy use. The algorithm’s adaptability means it can simulate both short-term (daily or weekly) and long-term (quarterly or yearly) targets, making it ideal for Q4 where fast results matter most. The key strength of the NSGA-II-based optimization is its ability to process conflicting objectives at once—like increasing output without compromising sustainability or overusing equipment. By applying it in Q4, mine planners can quickly identify where the system is underperforming and implement changes that produce measurable improvements before the year closes. This makes it not only a performance-boosting project but also a **strategic preparation tool for the next year’s operational plan**. Ultimately, a Q4 Production Optimization Project following this model uses real data, computational intelligence, and practical engineering to transform end-of-year pressure into measurable gains. It enhances the mine’s economic performance, ensures better resource utilization, and supports long-term sustainability goals—all while keeping within the constraints of existing resources and time. The improved NSGA-II framework acts as a decision-support tool that shows which levers to pull and how far, giving managers clear direction on where the greatest return can be achieved in the shortest possible time.