不确定条件下机械产品设计方案费效权衡优化

Cost-effective trade-off of mechanical product design scheme under uncertainty

为解决由产品设计和实际生产过程中各种不确定性因素的影响导致的机械产品设计方案费效权衡结果出现偏差的问题,提出将不确定优化理论引入机械产品设计方案费效权衡中。在对产品关键性能参数敏感性分析的基础上,将受不确定性因素影响的敏感性参数以及费用估算的偏差描述为随机变量,以一定置信水平下的费效比为优化目标,构建基于以费用为独立变量的费效权衡随机机会约束规划模型,并采用嵌入Monte Carlo模拟的遗传算法求解,得到不确定条件下最优产品设计方案,最后以混凝土泵车系列产品为实例进行验证。研究结果表明,采用基于以费用为独立变量的费效权衡随机规划模型优化的产品设计方案,更能反映生产实际,可以在不确定条件下最大程度地实现产品全生命周期成本目标的有效控制。

The result of cost-effective trade-offs {or mechanical product design scheme was affected by uncertainty problems in the production process. To solve this problem, the uncertainty optimization theory was introduced to the decision-making of cost-effective trade-offs. Based on the sensitivity analysis of key performance parameters, the sensitivity variables and cost estimates deviation were described as random variables, and the stochastic chance-con- strained programming model was built with cost-effectiveness ratio at a certain confidence level as optimization goals. The optimal solution was obtained by using Monte Carlo Simulation combined Genetic Algorithm （MCS-GA）. An example with the concrete pump truck was given, and the results showed that the design program traded-off with stochastic programming model based on CAIV could better reflect the actual production, and the product life cycle cost targets of under uncertain conditions could be controlled at the maximum extent.