不确定条件下露天煤矿车辆优化调度的研究  

Research on Optimal Vehicle Scheduling in Open Mine Under Uncertainty

作　　者：周天沛[1] 杨丽娟[2] 孙伟[3] ZHOU Tian-pei;YANG Li-juan;SUN Wei(School of Mechanical and Electrical Engineering, Xuzhou College of industrial and technology, Xuzhou 221140, China;School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221000, China;School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221008, China)

机构地区：[1]徐州工业职业技术学院机电工程学院,江苏徐州221140 [2]徐州工程学院机电工程学院,江苏徐州221000 [3]中国矿业大学信控学院,江苏徐州221008

出　　处：《控制工程》2019年第7期1298-1303,共6页Control Engineering of China

基　　金：江苏省高校自然科学研究面上项目(16KJB480006);江苏高校“青蓝工程”中青年学术带头人培养对象资助项目

摘　　要：针对不确定条件下露天煤矿车辆优化调度的研究较少的现状,首先讨论了露天煤矿车辆调度的优化目标函数和约束条件,建立了随机期望值目标规划模型.在对该模型进行求解的过程中,针对粒子群优化算法容易陷于局部优化值的缺点,引入了混沌理论,提出一种自适应混沌粒子群优化算法.将该算法用于一露天煤矿的车辆调度中,与传统粒子群算法相比,该算法能够有效提高全局收敛性.In view of the present situation that research on optimal vehicle scheduling in open mine under uncertainty is less, the optimization objective function and constrains of vehicle scheduling are discussed firstly, and then the stochastic expected value goal programming model is established. In the process of solving the model, the chaotic theory is applied because of local convergence of PSO algorithm, and adaptive chaotic PSO algorithm is proposed. The proposed algorithm is applied to the vehicle scheduling in an open mine, which can enhances the global convergence effectively compared with the traditional PSO algorithm.

关 键 词：露天煤矿 车辆调度 不确定优化 自适应混沌粒子群优化算法 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]