基于改进粒子群算法的供应商参与可靠性设计优化

研究供应商参与下的汽车产品子系统可靠性设计的优化问题，考虑供应商参与产品设计的可信度因素，建立以最大化系统的可靠度和供应商的可信度为优化目标的多目标数学规划模型。通过加权的方法把多目标优化模型转化为单目标非线性整数规划模型。采用粒子群(Particle swarm optimization，PSO)算法进行求解，提出适用于“零部件—供应商”关系的离散粒子编码方法。设计带有自适应动态惩罚项的适应度函数，把优化问题转化为无约束优化问题，并将粒子的搜索范围扩展到近可行解空间，进而较好地改进了算法的搜索速度和收敛性能。以某中级轿车传动系统零部件可靠性设计的优化问题为实例，进行仿真研究，应用质量功能展开和模糊评判的方法生成了零部件的权重和供应商可信度初始数据值，仿真结果验证了所提出PSO算法的实用性和有效性。

Suppliers-involved Reliability Design Optimization Based on an Improved Particle Swarm Optimization Algorithm

The reliability design problem of the automobile product subsystem in which the suppliers are involved is researched. The mathematic model in which the supplier creditability is considered includes two objectives: maximizing the subsystem reliability and the supplier creditability. The multi-objective model is transformed into the single objective nonlinear integer programming model by using a weighting method. This problem is solved by particle swarm optimization (PSO) algorithm where discrete particles are encoded to represent the “part-supplier” relationships. Then the self-adaptive objective function with a dynamic penalty factor is defined, which converts the optimization problem into the equivalent non-constraint problem. Such an objective function extends the search scope of particles to the near feasibility region and further improves the search speed and convergence performance as well. A case study on the reliability design optimization problem of the transmission system for the medium car is investigated. The original data about weights of the parts and the supplier creditability are obtained by applying the methods of quality function deployment and fuzzy synthesis evaluation. Simulation results demonstrate the practicability and the effectiveness of the proposed PSO.