基于随机规划的制造/再制造物流网络优化设计

针对含有连续型随机参数的制造/再制造物流网络优化设计问题,在Monte Carlo模拟抽样基础上,建立了样本数量决定解算效率的两阶段随机规划模型,给出了模型求解的混合遗传算法,结合样本均值近似方法阐述了获取理想目标值及其可行解的最优值上下界逼近技术,明确了基于两阶段随机规划的物流网络优化设计步骤,举例说明了模型及其算法在设计决策中的应用。

Optimal Design of Manufacturing/remanufacturing Logistics Network Based on Stochastic Programming

To deal with optimal design problem of manufacturing/remanufacturing logistics network involving stochastic parameters of continuous distribution, a two-stage stochastic programming model was designed by the way of Monte Carlo simulation sampling techniques. The model's computation efficiency was administrated by sample quantity, and the model could be solved by the calculations of mixed genetic algorithm. Combining sample average approximation method, optimal value's upper and lower bounds approaching techniques were expounded to acquire good objective value and corresponding feasible solutions, logistics network's optimal design steps were summarized based on two-stage stochastic programming, and also an example was illustrated to show the proposed model and its algorithm's applications in design decision-making.