基于遗传算法的多阶段动态批量生产问题研究

Research on multistage dynamic batch production problem based on genetic algorithm

在生产系统的供应链中,有已有制造线和再制造线两个阶段,合理安排两个阶段的生产计划是实现成本最小化的关键。考虑库存费用、制造全新产品数量和再制造产品数量的因素下,建立面向再制造线和已有制造线两阶段的动态批量生产成本优化模型,针对这一模型设计了自适应遗传算法并结合实际案例对问题求解,给出了合理的生产计划,实验表明:该遗传算法对解决多产品的动态批量问题有良好效果。

In the supply chain of production system, there are two stages including already manufac-turing line and re-manufacturing line. Reasonable arrangement for the production plans of the two-stage is a key to minimize costs. Considering the factors, such as the cost of inventory, the number of new product manufacturing and the remanufacturing quantity of product, a dynamic batch produc-tion cost optimization model for the two-stage manufacturing line is designed. The adaptive genetic algorithm associated with the model is also given, which gives the reasonable production plan in practical cases. The given experiments show that the genetic algorithm is good for solving the prob-lem of dynamic multi-product batch production.