This article presents a customized genetic algorithms (GA) with new crossover and mutation operators to solve the short-term production scheduling problem of multi-mine mineral value chains (MVC). The preceding problem consists of determining the extraction sequence and destination allocation of blocks from all the mines collaboratively while closely meeting the quality and quantity requirements of the processing units subject to relevant technical and operational constraints. The short-term production scheduling is carried out at shorter scales wherein the operations are modeled in a great detail with large number of constraints. This makes the industry-scale instances of the problem computationally intractable for standard mixed-integer programming (MIP) solvers. Thus, a GA-based heuristic approach is developed to obtain near-optimal solutions to the large-scale instances of the problem in a reasonable amount of computational time. Computational experiments show that the developed GA-based method is a promising way to handle industry-scale instances of the problem. Moreover, the sensitivity analysis on various parameter combinations of crossover and mutation operators indicates that the customized global mutation operator, when used in combination with the customized crossover operators, took on average 12.5% less time than the customized local mutation operator to converge to a solution.

本文提出了一种具有新交叉和变异算子的定制遗传算法（GA），用于解决多矿矿产价值链（MVC）的短期生产调度问题。上述问题包括协同确定所有矿山块的提取顺序和目的地分配，同时密切满足受相关技术和操作约束的加工单元的质量和数量要求。短期生产调度以较短的规模进行，其中操作被非常详细地建模，具有大量约束。这使得标准混合整数规划 (MIP) 求解器在计算上难以解决该问题的行业规模实例。因此，开发了一种基于 GA 的启发式方法，以在合理的计算时间内为问题的大规模实例获得接近最优的解决方案。计算实验表明，所开发的基于 GA 的方法是处理行业规模问题实例的一种很有前途的方法。此外，对交叉和变异算子的各种参数组合的敏感性分析表明，定制的全局变异算子与定制的交叉算子结合使用时，平均比定制的局部变异算子收敛到一个解的时间减少了 12.5% .