求解复杂交互大规模库存调配问题的启发式策略
Heuristic Strategies for Solving Complex Interacting Large-Scale Stockpile Blending Problems
摘要：
库存调配问题是矿山生产调度中的一个重要组成部分，库存用于存储和调配原材料。从库存中混合材料的目标是根据可用材料创建包含最佳金属等级的精矿包。每个库存提供给给定包裹的材料量取决于一组矿山进度条件和客户需求。因此，该问题可以用公式表示为连续优化问题。在现实世界的应用中，需要几个约束来保证包裹满足下游客户的需求。由于混料问题的规模可能非常大，因此解决混料问题是一个挑战。我们引入了两个修复算子，将不可行解转化为不违反两个紧约束的解。此外，我们引入了一个多组件的适应度函数来解决大规模的库存混合问题，它可以最大限度地提高计划的金属量，并根据金属的使用量来保持库存之间的平衡。此外，我们研究了著名的方法，在本文中，这是用来解决连续空间上的优化问题，即差分进化（DE）算法。实验结果表明，无论是一个月问题还是大规模问题，结合两种工期修复方法的DE算法的结果值都明显优于真实世界实例的结果。
英文摘要：
The Stockpile blending problem is an important component of mine production scheduling, where stockpiles are used to store and blend raw material. The goal of blending material from stockpiles is to create parcels of concentrate which contain optimal metal grades based on the material available. The volume of material that each stockpile provides to a given parcel is dependent on a set of mine schedule conditions and customer demands. Therefore, the problem can be formulated as a continuous optimization problem. In the real-world application, there are several constraints required to guarantee parcels that meet the demand of downstream customers. It is a challenge in solving the stockpile blending problems since its scale can be very large. We introduce two repaired operators for the problems to convert the infeasible solutions into the solutions without violating the two tight constraints. Besides, we introduce a multi-component fitness function for solving the large-scale stockpile blending problem which can maximize the volume of metal over the plan and maintain the balance between stockpiles according to the usage of metal. Furthermore, we investigate the well-known approach in this paper, which is used to solve optimization problems over continuous space, namely the differential evolution (DE) algorithm. The experimental results show that the DE algorithm combined with two proposed duration repair methods is significantly better in terms of the values of results than the results on real-world instances for both one-month problems and large-scale problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
