工业工程

工业工程 ›› 2014, Vol. 17 ›› Issue (5): 93-98.

• 专题论述 • 上一篇    下一篇

产能约束下的多级装配供应链优化配置方法研究

  

  1. （1.广东工业大学　广东省计算机集成制造重点实验室，广东 广州 510006；
    2.香港大学 工业及制造系统工程系，中国 香港；3.华南农业大学 理学院，广东 广州 510642)
  • 出版日期:2014-10-31 发布日期:2014-12-01
  • 作者简介:雷涛（1988-），男，湖南省人，硕士研究生，主要研究方向为供应链优化、企业信息化.
  • 基金资助:

     国家自然科学基金资助项目(51105081)；广东省自然科学基金资助项目（S2012010010016）

An Optimal Configuration of Multi-echelon Assembly  Supply Chains Under Capacity Constraints

  1. (1. Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System, Guangdong University of Technology,
    Guangzhou 510006, China；2. Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong,
    Hong Kong ,China；3. College of Science, South China Agricultural University, Guangzhou 510642, China)
  • Online:2014-10-31 Published:2014-12-01

摘要： 针对具有产能约束的多级装配型供应链优化配置问题，在考虑安全库存与产能约束、净补货时间关系基础上，研究最优供应商选择和交货期设置方法。面向不确定需求，建立对具有最大产能约束的供应链优化配置混合规划数学模型，并提出一种有较高求解精度的改进遗传算法求解。研究结果表明：在价格和时间不具有优势的情况下，供应链应该避免选择剩余产能小的供应商；在需求波动较大的情况下，为节点设定宽松的交货期往往能够降低供应链成本。

关键词:  , 产能约束； 需求不确定； 供应链优化配置； 改进遗传算法

Abstract:  The configuration problem of multi-echelon supply chain with limited production capacity is discussed. Based on the relationship of safety stock with respect to both the production capacity and net replenishment time, the supplier selection and the corresponding service times are optimized. A mixed integer programming model has been proposed for solving the supply chain configuration problem under uncertain market demands and limited production capacity, while an improved Genetic Algorithm with higher solution precision is adopted for problem solving. Two observations have been obtained from the results. First, a supply chain should avoid selecting those suppliers with smaller remaining capacities yet without advantageous prices and service times. Second, in case of highly fluctuated demands, a supply chain should allow broader lead time ranges for suppliers to achieve lower SCC（supply chain configuration）cost.

Key words:  capacity constraint, demand uncertainty, supply chain optimization configuration, improved genetic algorithm