摘要
针对传统的供应链需求预测模型中信息结构呈分散状态、对各成员预测结构差别大、影响库存补充策略的问题,创新地将CPFR技术应用到了协同补货的方法研究中,建立了基于最佳库存和最佳运送周期之间的函数关系的数学模型,并在此基础上采用进化策略法对建立的模型求解.结果表明,随着各项参数值的提高,总成本也随之增加,即最佳运输周期与最佳库存量受各项参数值的影响,其结论也验证了此方法的有效性.
Because the information structure of conventional demand forecasting model for supply chain is in discrete state and the prediction structures of individuals are different greatly from each other, the inventory replenishment strategy is seriously affected. So, the policy CPFR （collaborative planning, forecasting and replenishment） is introduced firstly into the study on collaborative replenishment to develop the mathematic models of the function of optimum inventory in terms of optimum delivery period. Then, an evolution strategy （ES） is used to give the solution to the models. The results indicate that total cost increases with increasing parameter values, namely, the optimum delivery period and the optimum inventory are affected with the different parameter values. The conclusion verifies the validity of the policy proposed.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2007年第7期1049-1052,共4页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(70572088)
关键词
CPFR
协同预测
泊松分布
协同补货
进化算法
collaborative planning, forecasting and replenishment
collaborative forecasting
Poisson distribution
collaborative replenishment
evolution strategy
