摘要
针对仓库容量有限条件下的随机存贮管理问题,通过找出商品在销售进货过程中会出现的全部可能,确定得到总损失最小的方法,然后以最优订货点作为决策变量并确定约束条件,建立优化决策模型。采用基于随机模拟的混合智能算法对该决策模型进行求解。对模型中的不确定函数进行随机模拟,使用由其产生的一组输入输出数据来训练神经网络对该不确定函数进行逼近,然后将该神经网络作为适应度函数嵌套于遗传算法中,最后应用遗传算法解得模型的最优解。通过计算机仿真得到所需要的最优方案。实验表明,文中提出的基于混合智能算法的仓库随机存贮模型,较好地解决了实际应用中的仓库容量有限的随机存贮问题,具有很强的普遍性和实用性。
By finding out the method to scale general loss during the course of stocking and sales of goods, We Establish a decision-making model in order to solve the problem of Random Storage Management under the Condition of Limited Warehouse Capacity. And then using Hybrid intelligent algorithm based on random simulation to figure out this model. By random simulating the uncertainty function in this model, we can get a set of data, then a Neural Network can be trained by this data. Moreover, we can set the Neural Network as a adapted function in the GA algorithm, finally, Using this GA algorithm to solve the model. In this paper, we simulate this model under Matlab 6.5, and make a good result.
出处
《价值工程》
2007年第4期74-76,共3页
Value Engineering
关键词
随机存贮
随机模拟
优化决策模型
混合智能算法
random storage
random image processing
optimum decision-making model
hybrid intelligent algorithm
