Due to its better results over the traditional co-simulation methods, joint simulation of variables through orthogonal factors has gained popularity. In this approach, practitioners transform variables into orthogonal factors, simulate them independently, and back-transform the results into the initial space. It is unlikely to generate spatially uncorrelated factors at more than two lags. Therefore, the idea of simultaneous diagonalization has become the topic of so many studies. Some approaches have used the Jacobi transformation to replace a multivariate minimization problem with a sequence of univariate problems. However, these studies do not pay attention to the interruption of the previously minimized univariate problems while solving the new one. Therefore, this study aimed to minimize the objective function by considering all the univariate problems at once using the L-SHADE optimization method. The proposed method was applied to the Meiduk porphyry copper deposit to generate the L-SHADE factors. L-SHADE was efficient due to high speed of convergence and giving logical answer. Comparison among the L-SHADE factors and those of principal component analysis and Min/max autocorrelation factors showed better performance of the L-SHADE method such that the cross-variograms of the L-SHADE factors did not show noticeable spatial pattern and generally had smaller values. Sequential simulation was used to produce fifty equiprobable realizations from the L-SHADE factors. The proposed approach could reproduce the means, variances, correlation coefficients, cumulative distributions, and auto/cross-variograms of the variables in the simulations. Therefore, the simulations were reliable to be used in long-term production planning. A weighted multi-objective model using binary integer variables was developed to find the best possible production plan among different production scheduling alternatives developed based on the simulations. Different weighting scenarios were considered to investigate the impacts of economic (net present value), resources (ore extraction), and environmental (waste removal) objectives on stakeholder interests.

由于其优于传统联合仿真方法的结果，通过正交因素对变量进行联合仿真已得到普及。在这种方法中，从业者将变量转换为正交因子，独立模拟它们，然后将结果反变换到初始空间。不太可能在两个以上的滞后产生空间不相关的因素。因此，同时对角化的思想成为了众多研究的课题。一些方法使用雅可比变换来用一系列单变量问题代替多变量最小化问题。然而，这些研究在解决新问题时没有注意中断先前最小化的单变量问题。所以，本研究旨在通过使用 L-SHADE 优化方法同时考虑所有单变量问题来最小化目标函数。将所提出的方法应用于Meiduk斑岩铜矿床以生成L-SHADE因子。L-SHADE 是高效的，因为它的收敛速度很快并且给出了合乎逻辑的答案。L-SHADE 因子与主成分分析和 Min/max 自相关因子的比较表明 L-SHADE 方法具有更好的性能，L-SHADE 因子的交叉变异函数没有表现出明显的空间格局，并且通常具有较小的空间分布。值。顺序模拟用于从 L-SHADE 因子生成 50 个等概率实现。提议的方法可以重现均值、方差、相关系数、累积分布、以及模拟中变量的自动/交叉变异函数。因此，模拟是可靠的，可用于长期生产计划。开发了一个使用二进制整数变量的加权多目标模型，以在基于模拟开发的不同生产调度备选方案中寻找最佳生产计划。考虑了不同的加权情景来调查经济（净现值）、资源（矿石开采）和环境（废物清除）目标对利益相关者利益的影响。开发了一个使用二进制整数变量的加权多目标模型，以在基于模拟开发的不同生产调度备选方案中寻找最佳生产计划。考虑了不同的加权情景来调查经济（净现值）、资源（矿石开采）和环境（废物清除）目标对利益相关者利益的影响。开发了一个使用二进制整数变量的加权多目标模型，以在基于模拟开发的不同生产调度备选方案中寻找最佳生产计划。考虑了不同的加权情景来调查经济（净现值）、资源（矿石开采）和环境（废物清除）目标对利益相关者利益的影响。