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作 者:郭海湘[1] 诸克军[1] 李四福[1] 翁克瑞[1]
出 处:《系统工程理论与实践》2009年第11期135-144,共10页Systems Engineering-Theory & Practice
基 金:国家自然科学基金(70573101);高等学校博士学科点专项科研基金(20070491011);中国地质大学(武汉)优秀青年教师资助计划(CUGQNW0702)
摘 要:运用智能优化算法中的遗传算法(GA)、粒子群算法(PSO)和改进粒子群算法(MPSO)在网络图优化的基础上分别对平安五矿己二采区首采面开工的进度计划进行二次优化控制,其结果能够为煤矿相关管理和施工人员提供决策依据.整个优化过程包括两个部分:其一是在原计划基础上,通过计划评审法(PERT)得到己二采区首采面各个工序的时间参数和相应的网络图;其二是在网络图的基础上,以净现值NPV(Net present value)最大化作为进度安排目标,以各工序的开工日期为决策变量,以各工序之间的先后顺序和时间关系为约束,分别用GA、PSO和MPSO进行二次优化.结果表明:MPSO要优于GA和PSO并且优化后净现值比原计划多1497.4万元.In this paper,the intelligent optimization methods including genetic algorithm(GA),particle swarm optimization(PSO) and modified particle swarm optimization(MPSO) are used in optimizing the project scheduling of the second region of the first coal face of the fifth Ping'an Coal.The result of optimization is the basic information of management and decision-making for governors and builder.The process of optimization contains two parts in this paper:the first part is obtaining the time parameters of each operation and the network graph of the first coal face in the second region by PERT method which are based on the raw data;another part based on the network graph is the second optimization of which the objective is the maximal NPV(Net present value) and the starting dates of all operations are decision-making variables and operations' order and time are the constraints.The optimizing results show that MPSO is better than GA and PSO and the NPV based on optimized operations is more than original plan 1497.4 ten thousand RMB.
分 类 号:N945.22[自然科学总论—系统科学]
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