矿用物资装载问题的遗传算法研究

为了更好地增强矿山企业内部市场化管理水平，提高矿用物资的仓储、运输等效率，优化装载方案是一项非常重要的技术环节。在对矿用零散货物配装问题分析的基础上，以物资配送的经济效益最大化为目标，兼顾车辆的有效容积和承载量等方面的约束条件，建立了基于优化方法求解的数学模型，并引入模拟生物进化的遗传算法进行了实例研究。应用结果表明，遗传算法在求解矿用物资装载最优方案的过程中，具有收敛速度快、用时短等优点，且计算精度较高；遗传算法良好的适应性和强大的搜索性能，非常适合用于求解多条件约束问题。研究结果有助于矿用零散物资运配送效能的提升，从而促进矿山企业相关物资管理部门经营效益的进一步提高。

Research of Genetic Algorithms on Loading Issue of Mining Material

To enhance internal market management level in mine enterprise，and improve the storage and transportation efficiency of mining material，the research on optimizing loading pattern is a very important technical link.Based on the research of the assembly issue of mining scattered goods，the goal of goods distributing to maximize the economic benefit，considering the constraints of effective volume and burden quantity of vehicle，the mathematical model is established based on optimization method to solve，and the case is studied by introducing genetic algorithms on simulating biological evolution.The application results show that genetic algorithms have the advantages of fast convergence and high calculation accuracy in the course of solving optimal plan of mining equipment loading.Genetic algorithm is suitable to solve the multi-constraints problem because of its good adaptability and powerful search performance.The research in this paper is helpful to lift the efficiency of mining scattered material，and then，promote management benefit for the material management department in mine enterprise.