A novel hybrid optimization framework for short-term mine production scheduling: synergizing linear programming relaxation and genetic algorithms
提出一种将线性规划松弛嵌入遗传算法的混合方法，用于求解大规模短期矿山生产调度问题，能在合理时间内获得接近最优的解。
This paper presents a hybrid optimization methodology by embedding Linear Programming Techniques within Genetic Algorithms to efficiently solve real-scale instances of short-term mine production scheduling problem. The methodology involves the linear programming relaxation of the mixed-integer programming model of the short-term mine production scheduling problem and deploying a priority mechanism developed in this work to construct feasible schedules. This process of schedule generation is then embedded within the customized genetic algorithms to efficiently solve the industrial scale instances of short-term mine production scheduling problem. Computational experiments demonstrate that proposed hybrid optimization framework is an efficient framework to obtain near-optimal solutions to large-scale instances of short-term mine production scheduling problem within a reasonable computation time.