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管理者通常需要确定系统参数及决策变量,以达成预期目标。但优化参数较多时,往往导致求解精度较低,甚至无法求解。为提高优化效果,在定义影响度的基础上提出了影响优化分析方法。依据影响度结果,从众多系统参数中选取那些对系统目标影响较大的参数。将原优化问题转化为非线性规划问题,引入遗传算法优化控制序列和所选参数。以库存系统为例进行数值仿真计算,得到了不同预期、需求条件下的订单处理时间和订货规律。该方法有效减少了优化变量个数,可更准确地为管理者制定运作计划提供依据。
Managers often need to determine system parameters and decision variables to achieve their desired goals. However, when there are many optimization parameters, it often leads to low solution accuracy and can not even be solved. In order to improve the optimization effect, an impact analysis method is proposed based on the definition of influence degree. According to the result of influence degree, we select the parameters that affect the system target from many system parameters. The original optimization problem is transformed into a nonlinear programming problem, the introduction of genetic algorithms to optimize the control sequence and the selected parameters. Taking the inventory system as an example, numerical simulation is carried out, and the order processing time and ordering rules under different expectations and requirements are obtained. This method can effectively reduce the number of optimization variables and provide a basis for managers to make operational plans more accurately.